[rllib] Q-Mix implementation (Q-Mix, VDN, IQN, and Ape-X variants) (#3548)

doc/source/rllib-algorithms.rst

@@ -8,7 +8,7 @@ Distributed Prioritized Experience Replay (Ape-X)
 -------------------------------------------------
 `[paper] <https://arxiv.org/abs/1803.00933>`__
 `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/apex.py>`__
-Ape-X variations of DQN and DDPG (`APEX_DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/apex.py>`__, `APEX_DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ddpg/apex.py>`__ in RLlib) use a single GPU learner and many CPU workers for experience collection. Experience collection can scale to hundreds of CPU workers due to the distributed prioritization of experience prior to storage in replay buffers.
+Ape-X variations of DQN, DDPG, and QMIX (`APEX_DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/apex.py>`__, `APEX_DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ddpg/apex.py>`__, `APEX_QMIX <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/qmix/apex.py>`__) use a single GPU learner and many CPU workers for experience collection. Experience collection can scale to hundreds of CPU workers due to the distributed prioritization of experience prior to storage in replay buffers.
 
 Tuned examples: `PongNoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-apex.yaml>`__, `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-apex-ddpg.yaml>`__, `MountainCarContinuous-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/mountaincarcontinuous-apex-ddpg.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-apex.yaml>`__.
 
@@ -245,3 +245,18 @@ Tuned examples: `Humanoid-v1 <https://github.com/ray-project/ray/blob/master/pyt
    :language: python
    :start-after: __sphinx_doc_begin__
    :end-before: __sphinx_doc_end__
+
+QMIX Monotonic Value Factorisation (QMIX, VDN, IQN)
+---------------------------------------------------
+`[paper] <https://arxiv.org/abs/1803.11485>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/qmix/qmix.py>`__ Q-Mix is a specialized multi-agent algorithm. Code here is adapted from https://github.com/oxwhirl/pymarl_alpha  to integrate with RLlib multi-agent APIs. To use Q-Mix, you must specify an agent `grouping <https://github.com/ray-project/ray/blob/master/python/ray/rllib/env/group_agents_wrapper.py>`__ in the environment (see the `two-step game example <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/twostep_game.py>`__). Currently, all agents in the group must be homogeneous. The algorithm can be scaled by increasing the number of workers or using Ape-X.
+
+Q-Mix is implemented in `PyTorch <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/qmix/qmix_policy_graph.py>`__ and is currently *experimental*.
+
+Tuned examples: `Two-step game <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/twostep_game.py>`__
+
+**QMIX-specific configs** (see also `common configs <rllib-training.html#common-parameters>`__):
+
+.. literalinclude:: ../../python/ray/rllib/agents/qmix/qmix.py
+   :language: python
+   :start-after: __sphinx_doc_begin__
+   :end-before: __sphinx_doc_end__

doc/source/rllib-env.rst

@@ -112,7 +112,7 @@ Multi-Agent
 
 .. note::
 
-   Learn more about multi-agent reinforcement learning in RLlib by reading the `blog post <https://rise.cs.berkeley.edu/blog/scaling-multi-agent-rl-with-rllib/>`__.
+   Learn more about multi-agent reinforcement learning in RLlib by reading the `blog post <https://bair.berkeley.edu/blog/2018/12/12/rllib/>`__.
 
 A multi-agent environment is one which has multiple acting entities per step, e.g., in a traffic simulation, there may be multiple "car" and "traffic light" agents in the environment. The model for multi-agent in RLlib as follows: (1) as a user you define the number of policies available up front, and (2) a function that maps agent ids to policy ids. This is summarized by the below figure:
 
@@ -202,6 +202,16 @@ Here is a simple `example training script <https://github.com/ray-project/ray/bl
 
 To scale to hundreds of agents, MultiAgentEnv batches policy evaluations across multiple agents internally. It can also be auto-vectorized by setting ``num_envs_per_worker > 1``.
 
+Grouping Agents
+~~~~~~~~~~~~~~~
+
+It is common to have groups of agents in multi-agent RL. RLlib treats agent groups like a single agent with a Tuple action and observation space. The group agent can then be assigned to a single policy for centralized execution, or to specialized multi-agent policies such as `Q-Mix <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__ that implement centralized training but decentralized execution. You can use the ``MultiAgentEnv.with_agent_groups()`` method to define these groups:
+
+.. literalinclude:: ../../python/ray/rllib/env/multi_agent_env.py
+   :language: python
+   :start-after: __grouping_doc_begin__
+   :end-before: __grouping_doc_end__
+
 Variable-Sharing Between Policies
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/source/rllib-training.rst

@@ -77,7 +77,7 @@ In an example below, we train A2C by specifying 8 workers through the config fla
 Specifying Resources
 ~~~~~~~~~~~~~~~~~~~~
 
-You can control the degree of parallelism used by setting the ``num_workers`` hyperparameter for most agents. The number of GPUs the driver should use can be set via the ``num_gpus`` option. Similarly, the resource allocation to workers can be controlled via ``num_cpus_per_worker``, ``num_gpus_per_worker``, and ``custom_resources_per_worker``. The number of GPUs can be a fractional quantity to allocate only a fraction of a GPU. For example, with DQN you can pack five agents onto one GPU by setting ``num_gpus: 0.2``. Note that in Ray < 0.6.0 fractional GPU support requires setting the environment variable ``RAY_USE_XRAY=1``.
+You can control the degree of parallelism used by setting the ``num_workers`` hyperparameter for most agents. The number of GPUs the driver should use can be set via the ``num_gpus`` option. Similarly, the resource allocation to workers can be controlled via ``num_cpus_per_worker``, ``num_gpus_per_worker``, and ``custom_resources_per_worker``. The number of GPUs can be a fractional quantity to allocate only a fraction of a GPU. For example, with DQN you can pack five agents onto one GPU by setting ``num_gpus: 0.2``.
 
 .. image:: rllib-config.svg
 

doc/source/rllib.rst

@@ -68,6 +68,10 @@ Algorithms
 
    -  `Evolution Strategies <rllib-algorithms.html#evolution-strategies>`__
 
+*  Multi-agent / specialized
+
+   -  `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__
+
 Models and Preprocessors
 ------------------------
 * `RLlib Models and Preprocessors Overview <rllib-models.html>`__

python/ray/rllib/__init__.py

@@ -33,8 +33,8 @@ def _register_all():
 
     for key in [
             "PPO", "ES", "DQN", "APEX", "A3C", "PG", "DDPG", "APEX_DDPG",
-            "IMPALA", "ARS", "A2C", "__fake", "__sigmoid_fake_data",
-            "__parameter_tuning"
+            "IMPALA", "ARS", "A2C", "QMIX", "APEX_QMIX", "__fake",
+            "__sigmoid_fake_data", "__parameter_tuning"
     ]:
         from ray.rllib.agents.agent import get_agent_class
         register_trainable(key, get_agent_class(key))

python/ray/rllib/agents/agent.py

@@ -10,6 +10,7 @@ import pickle
 import six
 import tempfile
 import tensorflow as tf
+import traceback
 from types import FunctionType
 
 import ray
@@ -546,6 +547,14 @@ def _register_if_needed(env_object):
 def get_agent_class(alg):
     """Returns the class of a known agent given its name."""
 
+    try:
+        return _get_agent_class(alg)
+    except ImportError:
+        from ray.rllib.agents.mock import _agent_import_failed
+        return _agent_import_failed(traceback.format_exc())
+
+
+def _get_agent_class(alg):
     if alg == "DDPG":
         from ray.rllib.agents import ddpg
         return ddpg.DDPGAgent
@@ -579,6 +588,12 @@ def get_agent_class(alg):
     elif alg == "IMPALA":
         from ray.rllib.agents import impala
         return impala.ImpalaAgent
+    elif alg == "QMIX":
+        from ray.rllib.agents import qmix
+        return qmix.QMixAgent
+    elif alg == "APEX_QMIX":
+        from ray.rllib.agents import qmix
+        return qmix.ApexQMixAgent
     elif alg == "script":
         from ray.tune import script_runner
         return script_runner.ScriptRunner

python/ray/rllib/agents/ddpg/ddpg.py

@@ -8,12 +8,6 @@ from ray.rllib.agents.ddpg.ddpg_policy_graph import DDPGPolicyGraph
 from ray.rllib.utils.annotations import override
 from ray.rllib.utils.schedules import ConstantSchedule, LinearSchedule
 
-OPTIMIZER_SHARED_CONFIGS = [
-    "buffer_size", "prioritized_replay", "prioritized_replay_alpha",
-    "prioritized_replay_beta", "prioritized_replay_eps", "sample_batch_size",
-    "train_batch_size", "learning_starts"
-]
-
 # yapf: disable
 # __sphinx_doc_begin__
 DEFAULT_CONFIG = with_common_config({

python/ray/rllib/agents/dqn/dqn.py

@@ -117,12 +117,13 @@ class DQNAgent(Agent):
     _agent_name = "DQN"
     _default_config = DEFAULT_CONFIG
     _policy_graph = DQNPolicyGraph
+    _optimizer_shared_configs = OPTIMIZER_SHARED_CONFIGS
 
     @override(Agent)
     def _init(self):
         # Update effective batch size to include n-step
         adjusted_batch_size = max(self.config["sample_batch_size"],
-                                  self.config["n_step"])
+                                  self.config.get("n_step", 1))
         self.config["sample_batch_size"] = adjusted_batch_size
 
         self.exploration0 = self._make_exploration_schedule(-1)
@@ -131,7 +132,7 @@ class DQNAgent(Agent):
             for i in range(self.config["num_workers"])
         ]
 
-        for k in OPTIMIZER_SHARED_CONFIGS:
+        for k in self._optimizer_shared_configs:
             if self._agent_name != "DQN" and k in [
                     "schedule_max_timesteps", "beta_annealing_fraction",
                     "final_prioritized_replay_beta"

python/ray/rllib/agents/mock.py

@@ -100,3 +100,16 @@ class _ParameterTuningAgent(_MockAgent):
             timesteps_this_iter=self.config["iter_timesteps"],
             time_this_iter_s=self.config["iter_time"],
             info={})
+
+
+def _agent_import_failed(trace):
+    """Returns dummy agent class for if PyTorch etc. is not installed."""
+
+    class _AgentImportFailed(Agent):
+        _agent_name = "AgentImportFailed"
+        _default_config = with_common_config({})
+
+        def _setup(self, config):
+            raise ImportError(trace)
+
+    return _AgentImportFailed

python/ray/rllib/agents/qmix/README.md

@@ -0,0 +1 @@
+Code in this package is adapted from https://github.com/oxwhirl/pymarl_alpha.

python/ray/rllib/agents/qmix/__init__.py

@@ -0,0 +1,8 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from ray.rllib.agents.qmix.qmix import QMixAgent, DEFAULT_CONFIG
+from ray.rllib.agents.qmix.apex import ApexQMixAgent
+
+__all__ = ["QMixAgent", "ApexQMixAgent", "DEFAULT_CONFIG"]

python/ray/rllib/agents/qmix/apex.py

@@ -0,0 +1,55 @@
+"""Experimental: scalable Ape-X variant of QMIX"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from ray.rllib.agents.qmix.qmix import QMixAgent, DEFAULT_CONFIG as QMIX_CONFIG
+from ray.rllib.utils.annotations import override
+from ray.rllib.utils import merge_dicts
+
+APEX_QMIX_DEFAULT_CONFIG = merge_dicts(
+    QMIX_CONFIG,  # see also the options in qmix.py, which are also supported
+    {
+        "optimizer_class": "AsyncReplayOptimizer",
+        "optimizer": merge_dicts(
+            QMIX_CONFIG["optimizer"],
+            {
+                "max_weight_sync_delay": 400,
+                "num_replay_buffer_shards": 4,
+                "batch_replay": True,  # required for RNN. Disables prio.
+                "debug": False
+            }),
+        "num_gpus": 0,
+        "num_workers": 32,
+        "buffer_size": 2000000,
+        "learning_starts": 50000,
+        "train_batch_size": 512,
+        "sample_batch_size": 50,
+        "max_weight_sync_delay": 400,
+        "target_network_update_freq": 500000,
+        "timesteps_per_iteration": 25000,
+        "per_worker_exploration": True,
+        "min_iter_time_s": 30,
+    },
+)
+
+
+class ApexQMixAgent(QMixAgent):
+    """QMIX variant that uses the Ape-X distributed policy optimizer.
+
+    By default, this is configured for a large single node (32 cores). For
+    running in a large cluster, increase the `num_workers` config var.
+    """
+
+    _agent_name = "APEX_QMIX"
+    _default_config = APEX_QMIX_DEFAULT_CONFIG
+
+    @override(QMixAgent)
+    def update_target_if_needed(self):
+        # Ape-X updates based on num steps trained, not sampled
+        if self.optimizer.num_steps_trained - self.last_target_update_ts > \
+                self.config["target_network_update_freq"]:
+            self.local_evaluator.for_policy(lambda p: p.update_target())
+            self.last_target_update_ts = self.optimizer.num_steps_trained
+            self.num_target_updates += 1

python/ray/rllib/agents/qmix/mixers.py

@@ -0,0 +1,64 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+class VDNMixer(nn.Module):
+    def __init__(self):
+        super(VDNMixer, self).__init__()
+
+    def forward(self, agent_qs, batch):
+        return th.sum(agent_qs, dim=2, keepdim=True)
+
+
+class QMixer(nn.Module):
+    def __init__(self, n_agents, state_shape, mixing_embed_dim):
+        super(QMixer, self).__init__()
+
+        self.n_agents = n_agents
+        self.embed_dim = mixing_embed_dim
+        self.state_dim = int(np.prod(state_shape))
+
+        self.hyper_w_1 = nn.Linear(self.state_dim,
+                                   self.embed_dim * self.n_agents)
+        self.hyper_w_final = nn.Linear(self.state_dim, self.embed_dim)
+
+        # State dependent bias for hidden layer
+        self.hyper_b_1 = nn.Linear(self.state_dim, self.embed_dim)
+
+        # V(s) instead of a bias for the last layers
+        self.V = nn.Sequential(
+            nn.Linear(self.state_dim, self.embed_dim), nn.ReLU(),
+            nn.Linear(self.embed_dim, 1))
+
+    def forward(self, agent_qs, states):
+        """Forward pass for the mixer.
+
+        Arguments:
+            agent_qs: Tensor of shape [B, T, n_agents, n_actions]
+            states: Tensor of shape [B, T, state_dim]
+        """
+        bs = agent_qs.size(0)
+        states = states.reshape(-1, self.state_dim)
+        agent_qs = agent_qs.view(-1, 1, self.n_agents)
+        # First layer
+        w1 = th.abs(self.hyper_w_1(states))
+        b1 = self.hyper_b_1(states)
+        w1 = w1.view(-1, self.n_agents, self.embed_dim)
+        b1 = b1.view(-1, 1, self.embed_dim)
+        hidden = F.elu(th.bmm(agent_qs, w1) + b1)
+        # Second layer
+        w_final = th.abs(self.hyper_w_final(states))
+        w_final = w_final.view(-1, self.embed_dim, 1)
+        # State-dependent bias
+        v = self.V(states).view(-1, 1, 1)
+        # Compute final output
+        y = th.bmm(hidden, w_final) + v
+        # Reshape and return
+        q_tot = y.view(bs, -1, 1)
+        return q_tot

python/ray/rllib/agents/qmix/model.py

@@ -0,0 +1,28 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from torch import nn
+import torch.nn.functional as F
+
+
+# TODO(ekl) we should have common models for pytorch like we do for TF
+class RNNModel(nn.Module):
+    def __init__(self, obs_size, rnn_hidden_dim, n_actions):
+        nn.Module.__init__(self)
+        self.rnn_hidden_dim = rnn_hidden_dim
+        self.n_actions = n_actions
+        self.fc1 = nn.Linear(obs_size, rnn_hidden_dim)
+        self.rnn = nn.GRUCell(rnn_hidden_dim, rnn_hidden_dim)
+        self.fc2 = nn.Linear(rnn_hidden_dim, n_actions)
+
+    def init_hidden(self):
+        # make hidden states on same device as model
+        return self.fc1.weight.new(1, self.rnn_hidden_dim).zero_()
+
+    def forward(self, inputs, hidden_state):
+        x = F.relu(self.fc1(inputs.float()))
+        h_in = hidden_state.reshape(-1, self.rnn_hidden_dim)
+        h = self.rnn(x, h_in)
+        q = self.fc2(h)
+        return q, h

python/ray/rllib/agents/qmix/qmix.py

@@ -0,0 +1,92 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from ray.rllib.agents.agent import with_common_config
+from ray.rllib.agents.dqn.dqn import DQNAgent
+from ray.rllib.agents.qmix.qmix_policy_graph import QMixPolicyGraph
+
+# yapf: disable
+# __sphinx_doc_begin__
+DEFAULT_CONFIG = with_common_config({
+    # === QMix ===
+    # Mixing network. Either "qmix", "vdn", or None
+    "mixer": "qmix",
+    # Size of the mixing network embedding
+    "mixing_embed_dim": 32,
+    # Whether to use Double_Q learning
+    "double_q": True,
+    # Optimize over complete episodes by default.
+    "batch_mode": "complete_episodes",
+
+    # === Exploration ===
+    # Max num timesteps for annealing schedules. Exploration is annealed from
+    # 1.0 to exploration_fraction over this number of timesteps scaled by
+    # exploration_fraction
+    "schedule_max_timesteps": 100000,
+    # Number of env steps to optimize for before returning
+    "timesteps_per_iteration": 1000,
+    # Fraction of entire training period over which the exploration rate is
+    # annealed
+    "exploration_fraction": 0.1,
+    # Final value of random action probability
+    "exploration_final_eps": 0.02,
+    # Update the target network every `target_network_update_freq` steps.
+    "target_network_update_freq": 500,
+
+    # === Replay buffer ===
+    # Size of the replay buffer in steps.
+    "buffer_size": 10000,
+
+    # === Optimization ===
+    # Learning rate for adam optimizer
+    "lr": 0.0005,
+    # RMSProp alpha
+    "optim_alpha": 0.99,
+    # RMSProp epsilon
+    "optim_eps": 0.00001,
+    # If not None, clip gradients during optimization at this value
+    "grad_norm_clipping": 10,
+    # How many steps of the model to sample before learning starts.
+    "learning_starts": 1000,
+    # Update the replay buffer with this many samples at once. Note that
+    # this setting applies per-worker if num_workers > 1.
+    "sample_batch_size": 4,
+    # Size of a batched sampled from replay buffer for training. Note that
+    # if async_updates is set, then each worker returns gradients for a
+    # batch of this size.
+    "train_batch_size": 32,
+
+    # === Parallelism ===
+    # Number of workers for collecting samples with. This only makes sense
+    # to increase if your environment is particularly slow to sample, or if
+    # you"re using the Async or Ape-X optimizers.
+    "num_workers": 0,
+    # Optimizer class to use.
+    "optimizer_class": "SyncBatchReplayOptimizer",
+    # Whether to use a distribution of epsilons across workers for exploration.
+    "per_worker_exploration": False,
+    # Whether to compute priorities on workers.
+    "worker_side_prioritization": False,
+    # Prevent iterations from going lower than this time span
+    "min_iter_time_s": 1,
+
+    # === Model ===
+    "model": {
+        "lstm_cell_size": 64,
+        "max_seq_len": 999999,
+    },
+})
+# __sphinx_doc_end__
+# yapf: enable
+
+
+class QMixAgent(DQNAgent):
+    """QMix implementation in PyTorch."""
+
+    _agent_name = "QMIX"
+    _default_config = DEFAULT_CONFIG
+    _policy_graph = QMixPolicyGraph
+    _optimizer_shared_configs = [
+        "learning_starts", "buffer_size", "train_batch_size"
+    ]

python/ray/rllib/agents/qmix/qmix_policy_graph.py

@@ -0,0 +1,411 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from gym.spaces import Tuple, Discrete, Dict
+import logging
+import numpy as np
+import torch as th
+import torch.nn as nn
+from torch.optim import RMSprop
+from torch.distributions import Categorical
+
+import ray
+from ray.rllib.agents.qmix.mixers import VDNMixer, QMixer
+from ray.rllib.agents.qmix.model import RNNModel
+from ray.rllib.evaluation.policy_graph import PolicyGraph
+from ray.rllib.models.action_dist import TupleActions
+from ray.rllib.models.pytorch.misc import var_to_np
+from ray.rllib.models.lstm import chop_into_sequences
+from ray.rllib.models.model import _unpack_obs
+from ray.rllib.models.preprocessors import get_preprocessor
+from ray.rllib.env.constants import GROUP_REWARDS
+from ray.rllib.utils.annotations import override
+
+logger = logging.getLogger(__name__)
+
+
+class QMixLoss(nn.Module):
+    def __init__(self,
+                 model,
+                 target_model,
+                 mixer,
+                 target_mixer,
+                 n_agents,
+                 n_actions,
+                 double_q=True,
+                 gamma=0.99):
+        nn.Module.__init__(self)
+        self.model = model
+        self.target_model = target_model
+        self.mixer = mixer
+        self.target_mixer = target_mixer
+        self.n_agents = n_agents
+        self.n_actions = n_actions
+        self.double_q = double_q
+        self.gamma = gamma
+
+    def forward(self, rewards, actions, terminated, mask, obs, action_mask):
+        """Forward pass of the loss.
+
+        Arguments:
+            rewards: Tensor of shape [B, T-1, n_agents]
+            actions: Tensor of shape [B, T-1, n_agents]
+            terminated: Tensor of shape [B, T-1, n_agents]
+            mask: Tensor of shape [B, T-1, n_agents]
+            obs: Tensor of shape [B, T, n_agents, obs_size]
+            action_mask: Tensor of shape [B, T, n_agents, n_actions]
+        """
+
+        B, T = obs.size(0), obs.size(1)
+
+        # Calculate estimated Q-Values
+        mac_out = []
+        h = self.model.init_hidden().expand([B, self.n_agents, -1])
+        for t in range(T):
+            q, h = _mac(self.model, obs[:, t], h)
+            mac_out.append(q)
+        mac_out = th.stack(mac_out, dim=1)  # Concat over time
+
+        # Pick the Q-Values for the actions taken -> [B * n_agents, T-1]
+        chosen_action_qvals = th.gather(
+            mac_out[:, :-1], dim=3, index=actions.unsqueeze(3)).squeeze(3)
+
+        # Calculate the Q-Values necessary for the target
+        target_mac_out = []
+        target_h = self.target_model.init_hidden().expand(
+            [B, self.n_agents, -1])
+        for t in range(T):
+            target_q, target_h = _mac(self.target_model, obs[:, t], target_h)
+            target_mac_out.append(target_q)
+
+        # We don't need the first timesteps Q-Value estimate for targets
+        target_mac_out = th.stack(
+            target_mac_out[1:], dim=1)  # Concat across time
+
+        # Mask out unavailable actions
+        target_mac_out[action_mask[:, 1:] == 0] = -9999999
+
+        # Max over target Q-Values
+        if self.double_q:
+            # Get actions that maximise live Q (for double q-learning)
+            mac_out[action_mask == 0] = -9999999
+            cur_max_actions = mac_out[:, 1:].max(dim=3, keepdim=True)[1]
+            target_max_qvals = th.gather(target_mac_out, 3,
+                                         cur_max_actions).squeeze(3)
+        else:
+            target_max_qvals = target_mac_out.max(dim=3)[0]
+
+        # Mix
+        if self.mixer is not None:
+            # TODO(ekl) add support for handling global state? This is just
+            # treating the stacked agent obs as the state.
+            chosen_action_qvals = self.mixer(chosen_action_qvals, obs[:, :-1])
+            target_max_qvals = self.target_mixer(target_max_qvals, obs[:, 1:])
+
+        # Calculate 1-step Q-Learning targets
+        targets = rewards + self.gamma * (1 - terminated) * target_max_qvals
+
+        # Td-error
+        td_error = (chosen_action_qvals - targets.detach())
+
+        mask = mask.expand_as(td_error)
+
+        # 0-out the targets that came from padded data
+        masked_td_error = td_error * mask
+
+        # Normal L2 loss, take mean over actual data
+        loss = (masked_td_error**2).sum() / mask.sum()
+        return loss, mask, masked_td_error, chosen_action_qvals, targets
+
+
+class QMixPolicyGraph(PolicyGraph):
+    """QMix impl. Assumes homogeneous agents for now.
+
+    You must use MultiAgentEnv.with_agent_groups() to group agents
+    together for QMix. This creates the proper Tuple obs/action spaces and
+    populates the '_group_rewards' info field.
+
+    Action masking: to specify an action mask for individual agents, use a
+    dict space with an action_mask key, e.g. {"obs": ob, "action_mask": mask}.
+    The mask space must be `Box(0, 1, (n_actions,))`.
+    """
+
+    def __init__(self, obs_space, action_space, config):
+        _validate(obs_space, action_space)
+        config = dict(ray.rllib.agents.qmix.qmix.DEFAULT_CONFIG, **config)
+        self.config = config
+        self.observation_space = obs_space
+        self.action_space = action_space
+        self.n_agents = len(obs_space.original_space.spaces)
+        self.n_actions = action_space.spaces[0].n
+        self.h_size = config["model"]["lstm_cell_size"]
+
+        agent_obs_space = obs_space.original_space.spaces[0]
+        if isinstance(agent_obs_space, Dict):
+            space_keys = set(agent_obs_space.spaces.keys())
+            if space_keys != {"obs", "action_mask"}:
+                raise ValueError(
+                    "Dict obs space for agent must have keyset "
+                    "['obs', 'action_mask'], got {}".format(space_keys))
+            mask_shape = tuple(agent_obs_space.spaces["action_mask"].shape)
+            if mask_shape != (self.n_actions, ):
+                raise ValueError("Action mask shape must be {}, got {}".format(
+                    (self.n_actions, ), mask_shape))
+            self.has_action_mask = True
+            self.obs_size = _get_size(agent_obs_space.spaces["obs"])
+        else:
+            self.has_action_mask = False
+            self.obs_size = _get_size(agent_obs_space)
+
+        self.model = RNNModel(self.obs_size, self.h_size, self.n_actions)
+        self.target_model = RNNModel(self.obs_size, self.h_size,
+                                     self.n_actions)
+
+        # Setup the mixer network.
+        # The global state is just the stacked agent observations for now.
+        self.state_shape = [self.obs_size, self.n_agents]
+        if config["mixer"] is None:
+            self.mixer = None
+            self.target_mixer = None
+        elif config["mixer"] == "qmix":
+            self.mixer = QMixer(self.n_agents, self.state_shape,
+                                config["mixing_embed_dim"])
+            self.target_mixer = QMixer(self.n_agents, self.state_shape,
+                                       config["mixing_embed_dim"])
+        elif config["mixer"] == "vdn":
+            self.mixer = VDNMixer()
+            self.target_mixer = VDNMixer()
+        else:
+            raise ValueError("Unknown mixer type {}".format(config["mixer"]))
+
+        self.cur_epsilon = 1.0
+        self.update_target()  # initial sync
+
+        # Setup optimizer
+        self.params = list(self.model.parameters())
+        self.loss = QMixLoss(self.model, self.target_model, self.mixer,
+                             self.target_mixer, self.n_agents, self.n_actions,
+                             self.config["double_q"], self.config["gamma"])
+        self.optimiser = RMSprop(
+            params=self.params,
+            lr=config["lr"],
+            alpha=config["optim_alpha"],
+            eps=config["optim_eps"])
+
+    @override(PolicyGraph)
+    def compute_actions(self,
+                        obs_batch,
+                        state_batches=None,
+                        prev_action_batch=None,
+                        prev_reward_batch=None,
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
+        obs_batch, action_mask = self._unpack_observation(obs_batch)
+        assert len(state_batches) == self.n_agents, state_batches
+        state_batches = np.stack(state_batches, axis=1)
+
+        # Compute actions
+        with th.no_grad():
+            q_values, hiddens = _mac(self.model, th.from_numpy(obs_batch),
+                                     th.from_numpy(state_batches))
+            avail = th.from_numpy(action_mask).float()
+            masked_q_values = q_values.clone()
+            masked_q_values[avail == 0.0] = -float("inf")
+            # epsilon-greedy action selector
+            random_numbers = th.rand_like(q_values[:, :, 0])
+            pick_random = (random_numbers < self.cur_epsilon).long()
+            random_actions = Categorical(avail).sample().long()
+            actions = (pick_random * random_actions +
+                       (1 - pick_random) * masked_q_values.max(dim=2)[1])
+            actions = var_to_np(actions)
+            hiddens = var_to_np(hiddens)
+
+        return (TupleActions(list(actions.transpose([1, 0]))),
+                hiddens.transpose([1, 0, 2]), {})
+
+    @override(PolicyGraph)
+    def compute_apply(self, samples):
+        obs_batch, action_mask = self._unpack_observation(samples["obs"])
+        group_rewards = self._get_group_rewards(samples["infos"])
+
+        # These will be padded to shape [B * T, ...]
+        [rew, action_mask, act, dones, obs], initial_states, seq_lens = \
+            chop_into_sequences(
+                samples["eps_id"],
+                samples["agent_index"], [
+                    group_rewards, action_mask, samples["actions"],
+                    samples["dones"], obs_batch
+                ],
+                [samples["state_in_{}".format(k)]
+                 for k in range(self.n_agents)],
+                max_seq_len=self.config["model"]["max_seq_len"],
+                dynamic_max=True,
+                _extra_padding=1)
+        # TODO(ekl) adding 1 extra unit of padding here, since otherwise we
+        # lose the terminating reward and the Q-values will be unanchored!
+        B, T = len(seq_lens), max(seq_lens) + 1
+
+        def to_batches(arr):
+            new_shape = [B, T] + list(arr.shape[1:])
+            return th.from_numpy(np.reshape(arr, new_shape))
+
+        rewards = to_batches(rew)[:, :-1].float()
+        actions = to_batches(act)[:, :-1].long()
+        obs = to_batches(obs).reshape([B, T, self.n_agents,
+                                       self.obs_size]).float()
+        action_mask = to_batches(action_mask)
+
+        # TODO(ekl) this treats group termination as individual termination
+        terminated = to_batches(dones.astype(np.float32)).unsqueeze(2).expand(
+            B, T, self.n_agents)[:, :-1]
+        filled = (np.reshape(np.tile(np.arange(T), B), [B, T]) <
+                  np.expand_dims(seq_lens, 1)).astype(np.float32)
+        mask = th.from_numpy(filled).unsqueeze(2).expand(B, T,
+                                                         self.n_agents)[:, :-1]
+        mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
+
+        # Compute loss
+        loss_out, mask, masked_td_error, chosen_action_qvals, targets = \
+            self.loss(rewards, actions, terminated, mask, obs, action_mask)
+
+        # Optimise
+        self.optimiser.zero_grad()
+        loss_out.backward()
+        grad_norm = th.nn.utils.clip_grad_norm_(
+            self.params, self.config["grad_norm_clipping"])
+        self.optimiser.step()
+
+        mask_elems = mask.sum().item()
+        stats = {
+            "loss": loss_out.item(),
+            "grad_norm": grad_norm
+            if isinstance(grad_norm, float) else grad_norm.item(),
+            "td_error_abs": masked_td_error.abs().sum().item() / mask_elems,
+            "q_taken_mean": (chosen_action_qvals * mask).sum().item() /
+            mask_elems,
+            "target_mean": (targets * mask).sum().item() / mask_elems,
+        }
+        return {"stats": stats}, {}
+
+    @override(PolicyGraph)
+    def get_initial_state(self):
+        return [
+            self.model.init_hidden().numpy().squeeze()
+            for _ in range(self.n_agents)
+        ]
+
+    @override(PolicyGraph)
+    def get_weights(self):
+        return {"model": self.model.state_dict()}
+
+    @override(PolicyGraph)
+    def set_weights(self, weights):
+        self.model.load_state_dict(weights["model"])
+
+    @override(PolicyGraph)
+    def get_state(self):
+        return {
+            "model": self.model.state_dict(),
+            "target_model": self.target_model.state_dict(),
+            "mixer": self.mixer.state_dict() if self.mixer else None,
+            "target_mixer": self.target_mixer.state_dict()
+            if self.mixer else None,
+            "cur_epsilon": self.cur_epsilon,
+        }
+
+    @override(PolicyGraph)
+    def set_state(self, state):
+        self.model.load_state_dict(state["model"])
+        self.target_model.load_state_dict(state["target_model"])
+        if state["mixer"] is not None:
+            self.mixer.load_state_dict(state["mixer"])
+            self.target_mixer.load_state_dict(state["target_mixer"])
+        self.set_epsilon(state["cur_epsilon"])
+        self.update_target()
+
+    def update_target(self):
+        self.target_model.load_state_dict(self.model.state_dict())
+        if self.mixer is not None:
+            self.target_mixer.load_state_dict(self.mixer.state_dict())
+        logger.debug("Updated target networks")
+
+    def set_epsilon(self, epsilon):
+        self.cur_epsilon = epsilon
+
+    def _get_group_rewards(self, info_batch):
+        group_rewards = np.array([
+            info.get(GROUP_REWARDS, [0.0] * self.n_agents)
+            for info in info_batch
+        ])
+        return group_rewards
+
+    def _unpack_observation(self, obs_batch):
+        unpacked = _unpack_obs(
+            np.array(obs_batch),
+            self.observation_space.original_space,
+            tensorlib=np)
+        if self.has_action_mask:
+            obs = np.concatenate(
+                [o["obs"] for o in unpacked],
+                axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
+            action_mask = np.concatenate(
+                [o["action_mask"] for o in unpacked], axis=1).reshape(
+                    [len(obs_batch), self.n_agents, self.n_actions])
+        else:
+            obs = np.concatenate(
+                unpacked,
+                axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
+            action_mask = np.ones(
+                [len(obs_batch), self.n_agents, self.n_actions])
+        return obs, action_mask
+
+
+def _validate(obs_space, action_space):
+    if not hasattr(obs_space, "original_space") or \
+            not isinstance(obs_space.original_space, Tuple):
+        raise ValueError("Obs space must be a Tuple, got {}. Use ".format(
+            obs_space) + "MultiAgentEnv.with_agent_groups() to group related "
+                         "agents for QMix.")
+    if not isinstance(action_space, Tuple):
+        raise ValueError(
+            "Action space must be a Tuple, got {}. ".format(action_space) +
+            "Use MultiAgentEnv.with_agent_groups() to group related "
+            "agents for QMix.")
+    if not isinstance(action_space.spaces[0], Discrete):
+        raise ValueError(
+            "QMix requires a discrete action space, got {}".format(
+                action_space.spaces[0]))
+    if len({str(x) for x in obs_space.original_space.spaces}) > 1:
+        raise ValueError(
+            "Implementation limitation: observations of grouped agents "
+            "must be homogeneous, got {}".format(
+                obs_space.original_space.spaces))
+    if len({str(x) for x in action_space.spaces}) > 1:
+        raise ValueError(
+            "Implementation limitation: action space of grouped agents "
+            "must be homogeneous, got {}".format(action_space.spaces))
+
+
+def _get_size(obs_space):
+    return get_preprocessor(obs_space)(obs_space).size
+
+
+def _mac(model, obs, h):
+    """Forward pass of the multi-agent controller.
+
+    Arguments:
+        model: Model that produces q-values for a 1d agent batch
+        obs: Tensor of shape [B, n_agents, obs_size]
+        h: Tensor of shape [B, n_agents, h_size]
+
+    Returns:
+        q_vals: Tensor of shape [B, n_agents, n_actions]
+        h: Tensor of shape [B, n_agents, h_size]
+    """
+    B, n_agents = obs.size(0), obs.size(1)
+    obs_flat = obs.reshape([B * n_agents, -1])
+    h_flat = h.reshape([B * n_agents, -1])
+    q_flat, h_flat = model.forward(obs_flat, h_flat)
+    return q_flat.reshape([B, n_agents, -1]), h_flat.reshape([B, n_agents, -1])

python/ray/rllib/env/async_vector_env.py

@@ -304,9 +304,9 @@ class _MultiAgentEnvToAsync(AsyncVectorEnv):
                 raise ValueError(
                     "Key set for obs and rewards must be the same: "
                     "{} vs {}".format(obs.keys(), rewards.keys()))
-            if set(obs.keys()) != set(infos.keys()):
-                raise ValueError("Key set for obs and infos must be the same: "
-                                 "{} vs {}".format(obs.keys(), infos.keys()))
+            if set(infos).difference(set(obs)):
+                raise ValueError("Key set for infos must be a subset of obs: "
+                                 "{} vs {}".format(infos.keys(), obs.keys()))
             if dones["__all__"]:
                 self.dones.add(env_id)
             self.env_states[env_id].observe(obs, rewards, dones, infos)

python/ray/rllib/env/constants.py

@@ -0,0 +1,19 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# info key for the individual rewards of an agent, for example:
+# info: {
+#   group_1: {
+#      _group_rewards: [5, -1, 1],  # 3 agents in this group
+#   }
+# }
+GROUP_REWARDS = "_group_rewards"
+
+# info key for the individual infos of an agent, for example:
+# info: {
+#   group_1: {
+#      _group_infos: [{"foo": ...}, {}],  # 2 agents in this group
+#   }
+# }
+GROUP_INFO = "_group_info"

python/ray/rllib/env/group_agents_wrapper.py

@@ -0,0 +1,107 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+
+from ray.rllib.env.constants import GROUP_REWARDS, GROUP_INFO
+from ray.rllib.env.multi_agent_env import MultiAgentEnv
+
+
+# TODO(ekl) we should add some unit tests for this
+class _GroupAgentsWrapper(MultiAgentEnv):
+    """Wraps a MultiAgentEnv environment with agents grouped as specified.
+
+    See multi_agent_env.py for the specification of groups.
+
+    This API is experimental.
+    """
+
+    def __init__(self, env, groups, obs_space=None, act_space=None):
+        """Wrap an existing multi-agent env to group agents together.
+
+        See MultiAgentEnv.with_agent_groups() for usage info.
+
+        Arguments:
+            env (MultiAgentEnv): env to wrap
+            groups (dict): Grouping spec as documented in MultiAgentEnv
+            obs_space (Space): Optional observation space for the grouped
+                env. Must be a tuple space.
+            act_space (Space): Optional action space for the grouped env.
+                Must be a tuple space.
+        """
+
+        self.env = env
+        self.groups = groups
+        self.agent_id_to_group = {}
+        for group_id, agent_ids in groups.items():
+            for agent_id in agent_ids:
+                if agent_id in self.agent_id_to_group:
+                    raise ValueError(
+                        "Agent id {} is in multiple groups".format(
+                            agent_id, groups))
+                self.agent_id_to_group[agent_id] = group_id
+        if obs_space is not None:
+            self.observation_space = obs_space
+        if act_space is not None:
+            self.action_space = act_space
+
+    def reset(self):
+        obs = self.env.reset()
+        return self._group_items(obs)
+
+    def step(self, action_dict):
+        # Ungroup and send actions
+        action_dict = self._ungroup_items(action_dict)
+        obs, rewards, dones, infos = self.env.step(action_dict)
+
+        # Apply grouping transforms to the env outputs
+        obs = self._group_items(obs)
+        rewards = self._group_items(
+            rewards, agg_fn=lambda gvals: list(gvals.values()))
+        dones = self._group_items(
+            dones, agg_fn=lambda gvals: all(gvals.values()))
+        infos = self._group_items(
+            infos, agg_fn=lambda gvals: {GROUP_INFO: list(gvals.values())})
+
+        # Aggregate rewards, but preserve the original values in infos
+        for agent_id, rew in rewards.items():
+            if isinstance(rew, list):
+                rewards[agent_id] = sum(rew)
+                if agent_id not in infos:
+                    infos[agent_id] = {}
+                infos[agent_id][GROUP_REWARDS] = rew
+
+        return obs, rewards, dones, infos
+
+    def _ungroup_items(self, items):
+        out = {}
+        for agent_id, value in items.items():
+            if agent_id in self.groups:
+                assert len(value) == len(self.groups[agent_id]), \
+                    (agent_id, value, self.groups)
+                for a, v in zip(self.groups[agent_id], value):
+                    out[a] = v
+            else:
+                out[agent_id] = value
+        return out
+
+    def _group_items(self, items, agg_fn=lambda gvals: list(gvals.values())):
+        grouped_items = {}
+        for agent_id, item in items.items():
+            if agent_id in self.agent_id_to_group:
+                group_id = self.agent_id_to_group[agent_id]
+                if group_id in grouped_items:
+                    continue  # already added
+                group_out = OrderedDict()
+                for a in self.groups[group_id]:
+                    if a in items:
+                        group_out[a] = items[a]
+                    else:
+                        raise ValueError(
+                            "Missing member of group {}: {}: {}".format(
+                                group_id, a, items))
+                grouped_items[group_id] = agg_fn(group_out)
+            else:
+                grouped_items[agent_id] = item
+        return grouped_items

python/ray/rllib/env/multi_agent_env.py

@@ -30,9 +30,14 @@ class MultiAgentEnv(object):
         }
         >>> print(dones)
         {
-            "car_0": False,
-            "car_1": True,
-            "__all__": False,
+            "car_0": False,    # car_0 is still running
+            "car_1": True,     # car_1 is done
+            "__all__": False,  # the env is not done
+        }
+        >>> print(infos)
+        {
+            "car_0": {},  # info for car_0
+            "car_1": {},  # info for car_1
         }
     """
 
@@ -57,6 +62,47 @@ class MultiAgentEnv(object):
                 episode is just started, the value will be None.
             dones (dict): Done values for each ready agent. The special key
                 "__all__" (required) is used to indicate env termination.
-            infos (dict): Info values for each ready agent.
+            infos (dict): Optional info values for each agent id.
         """
         raise NotImplementedError
+
+# yapf: disable
+# __grouping_doc_begin__
+    def with_agent_groups(self, groups, obs_space=None, act_space=None):
+        """Convenience method for grouping together agents in this env.
+
+        An agent group is a list of agent ids that are mapped to a single
+        logical agent. All agents of the group must act at the same time in the
+        environment. The grouped agent exposes Tuple action and observation
+        spaces that are the concatenated action and obs spaces of the
+        individual agents.
+
+        The rewards of all the agents in a group are summed. The individual
+        agent rewards are available under the "individual_rewards" key of the
+        group info return.
+
+        Agent grouping is required to leverage algorithms such as Q-Mix.
+
+        This API is experimental.
+
+        Arguments:
+            groups (dict): Mapping from group id to a list of the agent ids
+                of group members. If an agent id is not present in any group
+                value, it will be left ungrouped.
+            obs_space (Space): Optional observation space for the grouped
+                env. Must be a tuple space.
+            act_space (Space): Optional action space for the grouped env.
+                Must be a tuple space.
+
+        Examples:
+            >>> env = YourMultiAgentEnv(...)
+            >>> grouped_env = env.with_agent_groups(env, {
+            ...   "group1": ["agent1", "agent2", "agent3"],
+            ...   "group2": ["agent4", "agent5"],
+            ... })
+        """
+
+        from ray.rllib.env.group_agents_wrapper import _GroupAgentsWrapper
+        return _GroupAgentsWrapper(self, groups, obs_space, act_space)
+# __grouping_doc_end__
+# yapf: enable

python/ray/rllib/evaluation/policy_graph.py

@@ -42,7 +42,9 @@ class PolicyGraph(object):
                         state_batches,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        episodes=None):
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
         """Compute actions for the current policy.
 
         Arguments:
@@ -50,9 +52,11 @@ class PolicyGraph(object):
             state_batches (list): list of RNN state input batches, if any
             prev_action_batch (np.ndarray): batch of previous action values
             prev_reward_batch (np.ndarray): batch of previous rewards
+            info_batch (info): batch of info objects
             episodes (list): MultiAgentEpisode for each obs in obs_batch.
                 This provides access to all of the internal episode state,
                 which may be useful for model-based or multiagent algorithms.
+            kwargs: forward compatibility placeholder
 
         Returns:
             actions (np.ndarray): batch of output actions, with shape like
@@ -69,7 +73,9 @@ class PolicyGraph(object):
                               state,
                               prev_action_batch=None,
                               prev_reward_batch=None,
-                              episode=None):
+                              info_batch=None,
+                              episode=None,
+                              **kwargs):
         """Unbatched version of compute_actions.
 
         Arguments:
@@ -77,9 +83,11 @@ class PolicyGraph(object):
             state_batches (list): list of RNN state inputs, if any
             prev_action_batch (np.ndarray): batch of previous action values
             prev_reward_batch (np.ndarray): batch of previous rewards
+            info_batch (list): batch of info objects
             episode (MultiAgentEpisode): this provides access to all of the
                 internal episode state, which may be useful for model-based or
                 multi-agent algorithms.
+            kwargs: forward compatibility placeholder
 
         Returns:
             actions (obj): single action

python/ray/rllib/evaluation/sampler.py

@@ -24,20 +24,13 @@ RolloutMetrics = namedtuple(
     "RolloutMetrics",
     ["episode_length", "episode_reward", "agent_rewards", "custom_metrics"])
 
-PolicyEvalData = namedtuple(
-    "PolicyEvalData",
-    ["env_id", "agent_id", "obs", "rnn_state", "prev_action", "prev_reward"])
+PolicyEvalData = namedtuple("PolicyEvalData", [
+    "env_id", "agent_id", "obs", "info", "rnn_state", "prev_action",
+    "prev_reward"
+])
 
 
 class SyncSampler(object):
-    """This class interacts with the environment and tells it what to do.
-
-    Note that batch_size is only a unit of measure here. Batches can
-    accumulate and the gradient can be calculated on up to 5 batches.
-
-    This class provides data on invocation, rather than on a separate
-    thread."""
-
     def __init__(self,
                  env,
                  policies,
@@ -94,11 +87,6 @@ class SyncSampler(object):
 
 
 class AsyncSampler(threading.Thread):
-    """This class interacts with the environment and tells it what to do.
-
-    Note that batch_size is only a unit of measure here. Batches can
-    accumulate and the gradient can be calculated on up to 5 batches."""
-
     def __init__(self,
                  env,
                  policies,
@@ -361,17 +349,18 @@ def _process_observations(async_vector_env, policies, batch_builder_pool,
             if not agent_done:
                 to_eval[policy_id].append(
                     PolicyEvalData(env_id, agent_id, filtered_obs,
+                                   infos[env_id].get(agent_id, {}),
                                    episode.rnn_state_for(agent_id),
                                    episode.last_action_for(agent_id),
                                    rewards[env_id][agent_id] or 0.0))
 
             last_observation = episode.last_observation_for(agent_id)
             episode._set_last_observation(agent_id, filtered_obs)
-            episode._set_last_info(agent_id, infos[env_id][agent_id])
+            episode._set_last_info(agent_id, infos[env_id].get(agent_id, {}))
 
             # Record transition info if applicable
-            if last_observation is not None and \
-                    infos[env_id][agent_id].get("training_enabled", True):
+            if (last_observation is not None and infos[env_id].get(
+                    agent_id, {}).get("training_enabled", True)):
                 episode.batch_builder.add_values(
                     agent_id,
                     policy_id,
@@ -384,7 +373,7 @@ def _process_observations(async_vector_env, policies, batch_builder_pool,
                     prev_actions=episode.prev_action_for(agent_id),
                     prev_rewards=episode.prev_reward_for(agent_id),
                     dones=agent_done,
-                    infos=infos[env_id][agent_id],
+                    infos=infos[env_id].get(agent_id, {}),
                     new_obs=filtered_obs,
                     **episode.last_pi_info_for(agent_id))
 
@@ -435,6 +424,7 @@ def _process_observations(async_vector_env, policies, batch_builder_pool,
                     to_eval[policy_id].append(
                         PolicyEvalData(
                             env_id, agent_id, filtered_obs,
+                            episode.last_info_for(agent_id) or {},
                             episode.rnn_state_for(agent_id),
                             np.zeros_like(
                                 _flatten_action(policy.action_space.sample())),
@@ -462,6 +452,7 @@ def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
         policy = _get_or_raise(policies, policy_id)
         if builder and (policy.compute_actions.__code__ is
                         TFPolicyGraph.compute_actions.__code__):
+            # TODO(ekl): how can we make info batch available to TF code?
             pending_fetches[policy_id] = policy._build_compute_actions(
                 builder, [t.obs for t in eval_data],
                 rnn_in_cols,
@@ -473,6 +464,7 @@ def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
                 rnn_in_cols,
                 prev_action_batch=[t.prev_action for t in eval_data],
                 prev_reward_batch=[t.prev_reward for t in eval_data],
+                info_batch=[t.info for t in eval_data],
                 episodes=[active_episodes[t.env_id] for t in eval_data])
     if builder:
         for k, v in pending_fetches.items():

python/ray/rllib/evaluation/tf_policy_graph.py

@@ -153,7 +153,9 @@ class TFPolicyGraph(PolicyGraph):
                         state_batches=None,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        episodes=None):
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
         builder = TFRunBuilder(self._sess, "compute_actions")
         fetches = self._build_compute_actions(builder, obs_batch,
                                               state_batches, prev_action_batch,

python/ray/rllib/evaluation/torch_policy_graph.py

@@ -63,7 +63,9 @@ class TorchPolicyGraph(PolicyGraph):
                         state_batches=None,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        episodes=None):
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
         if state_batches:
             raise NotImplementedError("Torch RNN support")
         with self.lock:

python/ray/rllib/examples/twostep_game.py

@@ -0,0 +1,117 @@
+"""The two-step game from QMIX: https://arxiv.org/pdf/1803.11485.pdf"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+from gym.spaces import Tuple, Discrete
+
+import ray
+from ray.tune import register_env, run_experiments, grid_search
+from ray.rllib.env.multi_agent_env import MultiAgentEnv
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--stop", type=int, default=50000)
+parser.add_argument("--run", type=str, default="QMIX")
+
+
+class TwoStepGame(MultiAgentEnv):
+    action_space = Discrete(2)
+
+    # Each agent gets a separate [3] obs space, to ensure that they can
+    # learn meaningfully different Q values even with a shared Q model.
+    observation_space = Discrete(6)
+
+    def __init__(self, env_config):
+        self.state = None
+
+    def reset(self):
+        self.state = 0
+        return {"agent_1": self.state, "agent_2": self.state + 3}
+
+    def step(self, action_dict):
+        if self.state == 0:
+            action = action_dict["agent_1"]
+            assert action in [0, 1], action
+            if action == 0:
+                self.state = 1
+            else:
+                self.state = 2
+            global_rew = 0
+            done = False
+        elif self.state == 1:
+            global_rew = 7
+            done = True
+        else:
+            if action_dict["agent_1"] == 0 and action_dict["agent_2"] == 0:
+                global_rew = 0
+            elif action_dict["agent_1"] == 1 and action_dict["agent_2"] == 1:
+                global_rew = 8
+            else:
+                global_rew = 1
+            done = True
+
+        rewards = {"agent_1": global_rew / 2.0, "agent_2": global_rew / 2.0}
+        obs = {"agent_1": self.state, "agent_2": self.state + 3}
+        dones = {"__all__": done}
+        infos = {}
+        return obs, rewards, dones, infos
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    grouping = {
+        "group_1": ["agent_1", "agent_2"],
+    }
+    obs_space = Tuple([
+        TwoStepGame.observation_space,
+        TwoStepGame.observation_space,
+    ])
+    act_space = Tuple([
+        TwoStepGame.action_space,
+        TwoStepGame.action_space,
+    ])
+    register_env(
+        "grouped_twostep",
+        lambda config: TwoStepGame(config).with_agent_groups(
+            grouping, obs_space=obs_space, act_space=act_space))
+
+    if args.run == "QMIX":
+        config = {
+            "sample_batch_size": 4,
+            "train_batch_size": 32,
+            "exploration_final_eps": 0.0,
+            "num_workers": 0,
+            "mixer": grid_search([None, "qmix", "vdn"]),
+        }
+    elif args.run == "APEX_QMIX":
+        config = {
+            "num_gpus": 0,
+            "num_workers": 2,
+            "optimizer": {
+                "num_replay_buffer_shards": 1,
+            },
+            "min_iter_time_s": 3,
+            "buffer_size": 1000,
+            "learning_starts": 1000,
+            "train_batch_size": 128,
+            "sample_batch_size": 32,
+            "target_network_update_freq": 500,
+            "timesteps_per_iteration": 1000,
+        }
+    else:
+        config = {}
+
+    ray.init()
+    run_experiments({
+        "two_step": {
+            "run": args.run,
+            "env": "grouped_twostep",
+            "stop": {
+                "timesteps_total": args.stop,
+            },
+            "config": config,
+        },
+    })

python/ray/rllib/models/lstm.py

@@ -123,7 +123,8 @@ def chop_into_sequences(episode_ids,
                         feature_columns,
                         state_columns,
                         max_seq_len,
-                        dynamic_max=True):
+                        dynamic_max=True,
+                        _extra_padding=0):
     """Truncate and pad experiences into fixed-length sequences.
 
     Arguments:
@@ -136,6 +137,7 @@ def chop_into_sequences(episode_ids,
         dynamic_max (bool): Whether to dynamically shrink the max seq len.
             For example, if max len is 20 and the actual max seq len in the
             data is 7, it will be shrunk to 7.
+        _extra_padding (int): Add extra padding to the end of sequences.
 
     Returns:
         f_pad (list): Padded feature columns. These will be of shape
@@ -177,7 +179,7 @@ def chop_into_sequences(episode_ids,
 
     # Dynamically shrink max len as needed to optimize memory usage
     if dynamic_max:
-        max_seq_len = max(seq_lens)
+        max_seq_len = max(seq_lens) + _extra_padding
 
     feature_sequences = []
     for f in feature_columns:

python/ray/rllib/models/model.py

@@ -168,7 +168,15 @@ def _restore_original_dimensions(input_dict, obs_space):
     return input_dict
 
 
-def _unpack_obs(obs, space):
+def _unpack_obs(obs, space, tensorlib=tf):
+    """Unpack a flattened Dict or Tuple observation array/tensor.
+
+    Arguments:
+        obs: The flattened observation tensor
+        space: The original space prior to flattening
+        tensorlib: The library used to unflatten (reshape) the array/tensor
+    """
+
     if (isinstance(space, gym.spaces.Dict)
             or isinstance(space, gym.spaces.Tuple)):
         prep = get_preprocessor(space)(space)
@@ -186,14 +194,18 @@ def _unpack_obs(obs, space):
                 offset += p.size
                 u.append(
                     _unpack_obs(
-                        tf.reshape(obs_slice, [-1] + list(p.shape)), v))
+                        tensorlib.reshape(obs_slice, [-1] + list(p.shape)),
+                        v,
+                        tensorlib=tensorlib))
         else:
             u = OrderedDict()
             for p, (k, v) in zip(prep.preprocessors, space.spaces.items()):
                 obs_slice = obs[:, offset:offset + p.size]
                 offset += p.size
                 u[k] = _unpack_obs(
-                    tf.reshape(obs_slice, [-1] + list(p.shape)), v)
+                    tensorlib.reshape(obs_slice, [-1] + list(p.shape)),
+                    v,
+                    tensorlib=tensorlib)
         return u
     else:
         return obs

python/ray/rllib/optimizers/__init__.py

@@ -5,10 +5,17 @@ from ray.rllib.optimizers.async_gradients_optimizer import \
     AsyncGradientsOptimizer
 from ray.rllib.optimizers.sync_samples_optimizer import SyncSamplesOptimizer
 from ray.rllib.optimizers.sync_replay_optimizer import SyncReplayOptimizer
+from ray.rllib.optimizers.sync_batch_replay_optimizer import \
+    SyncBatchReplayOptimizer
 from ray.rllib.optimizers.multi_gpu_optimizer import LocalMultiGPUOptimizer
 
 __all__ = [
-    "PolicyOptimizer", "AsyncReplayOptimizer", "AsyncSamplesOptimizer",
-    "AsyncGradientsOptimizer", "SyncSamplesOptimizer", "SyncReplayOptimizer",
-    "LocalMultiGPUOptimizer"
+    "PolicyOptimizer",
+    "AsyncReplayOptimizer",
+    "AsyncSamplesOptimizer",
+    "AsyncGradientsOptimizer",
+    "SyncSamplesOptimizer",
+    "SyncReplayOptimizer",
+    "LocalMultiGPUOptimizer",
+    "SyncBatchReplayOptimizer",
 ]

python/ray/rllib/optimizers/async_replay_optimizer.py

@@ -36,6 +36,11 @@ class AsyncReplayOptimizer(PolicyOptimizer):
     This class coordinates the data transfers between the learner thread,
     remote evaluators (Ape-X actors), and replay buffer actors.
 
+    This has two modes of operation:
+        - normal replay: replays independent samples.
+        - batch replay: simplified mode where entire sample batches are
+            replayed. This supports RNNs, but not prioritization.
+
     This optimizer requires that policy evaluators return an additional
     "td_error" array in the info return of compute_gradients(). This error
     term will be used for sample prioritization."""
@@ -52,9 +57,11 @@ class AsyncReplayOptimizer(PolicyOptimizer):
               sample_batch_size=50,
               num_replay_buffer_shards=1,
               max_weight_sync_delay=400,
-              debug=False):
+              debug=False,
+              batch_replay=False):
 
         self.debug = debug
+        self.batch_replay = batch_replay
         self.replay_starts = learning_starts
         self.prioritized_replay_beta = prioritized_replay_beta
         self.prioritized_replay_eps = prioritized_replay_eps
@@ -63,7 +70,11 @@ class AsyncReplayOptimizer(PolicyOptimizer):
         self.learner = LearnerThread(self.local_evaluator)
         self.learner.start()
 
-        self.replay_actors = create_colocated(ReplayActor, [
+        if self.batch_replay:
+            replay_cls = BatchReplayActor
+        else:
+            replay_cls = ReplayActor
+        self.replay_actors = create_colocated(replay_cls, [
             num_replay_buffer_shards,
             learning_starts,
             buffer_size,
@@ -101,6 +112,7 @@ class AsyncReplayOptimizer(PolicyOptimizer):
 
     @override(PolicyOptimizer)
     def step(self):
+        assert self.learner.is_alive()
         assert len(self.remote_evaluators) > 0
         start = time.time()
         sample_timesteps, train_timesteps = self._step()
@@ -299,6 +311,54 @@ class ReplayActor(object):
         return stat
 
 
+@ray.remote(num_cpus=0)
+class BatchReplayActor(object):
+    """The batch replay version of the replay actor.
+
+    This allows for RNN models, but ignores prioritization params.
+    """
+
+    def __init__(self, num_shards, learning_starts, buffer_size,
+                 train_batch_size, prioritized_replay_alpha,
+                 prioritized_replay_beta, prioritized_replay_eps):
+        self.replay_starts = learning_starts // num_shards
+        self.buffer_size = buffer_size // num_shards
+        self.train_batch_size = train_batch_size
+        self.buffer = []
+
+        # Metrics
+        self.num_added = 0
+        self.cur_size = 0
+
+    def get_host(self):
+        return os.uname()[1]
+
+    def add_batch(self, batch):
+        # Handle everything as if multiagent
+        if isinstance(batch, SampleBatch):
+            batch = MultiAgentBatch({DEFAULT_POLICY_ID: batch}, batch.count)
+        self.buffer.append(batch)
+        self.cur_size += batch.count
+        self.num_added += batch.count
+        while self.cur_size > self.buffer_size:
+            self.cur_size -= self.buffer.pop(0).count
+
+    def replay(self):
+        if self.num_added < self.replay_starts:
+            return None
+        return random.choice(self.buffer)
+
+    def update_priorities(self, prio_dict):
+        pass
+
+    def stats(self, debug=False):
+        stat = {
+            "cur_size": self.cur_size,
+            "num_added": self.num_added,
+        }
+        return stat
+
+
 class LearnerThread(threading.Thread):
     """Background thread that updates the local model from replay data.
 
@@ -334,8 +394,8 @@ class LearnerThread(threading.Thread):
                 grad_out = self.local_evaluator.compute_apply(replay)
                 for pid, info in grad_out.items():
                     prio_dict[pid] = (
-                        replay.policy_batches[pid]["batch_indexes"],
-                        info["td_error"])
+                        replay.policy_batches[pid].data.get("batch_indexes"),
+                        info.get("td_error"))
                     if "stats" in info:
                         self.stats[pid] = info["stats"]
             self.outqueue.put((ra, prio_dict, replay.count))

python/ray/rllib/optimizers/sync_batch_replay_optimizer.py

@@ -0,0 +1,101 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import random
+
+import ray
+from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
+from ray.rllib.evaluation.sample_batch import SampleBatch, DEFAULT_POLICY_ID, \
+    MultiAgentBatch
+from ray.rllib.utils.annotations import override
+from ray.rllib.utils.timer import TimerStat
+
+
+class SyncBatchReplayOptimizer(PolicyOptimizer):
+    """Variant of the sync replay optimizer that replays entire batches.
+
+    This enables RNN support. Does not currently support prioritization."""
+
+    @override(PolicyOptimizer)
+    def _init(self,
+              learning_starts=1000,
+              buffer_size=10000,
+              train_batch_size=32):
+        self.replay_starts = learning_starts
+        self.max_buffer_size = buffer_size
+        self.train_batch_size = train_batch_size
+        assert self.max_buffer_size >= self.replay_starts
+
+        # List of buffered sample batches
+        self.replay_buffer = []
+        self.buffer_size = 0
+
+        # Stats
+        self.update_weights_timer = TimerStat()
+        self.sample_timer = TimerStat()
+        self.grad_timer = TimerStat()
+        self.learner_stats = {}
+
+    @override(PolicyOptimizer)
+    def step(self):
+        with self.update_weights_timer:
+            if self.remote_evaluators:
+                weights = ray.put(self.local_evaluator.get_weights())
+                for e in self.remote_evaluators:
+                    e.set_weights.remote(weights)
+
+        with self.sample_timer:
+            if self.remote_evaluators:
+                batches = ray.get(
+                    [e.sample.remote() for e in self.remote_evaluators])
+            else:
+                batches = [self.local_evaluator.sample()]
+
+            # Handle everything as if multiagent
+            tmp = []
+            for batch in batches:
+                if isinstance(batch, SampleBatch):
+                    batch = MultiAgentBatch({
+                        DEFAULT_POLICY_ID: batch
+                    }, batch.count)
+                tmp.append(batch)
+            batches = tmp
+
+            for batch in batches:
+                self.replay_buffer.append(batch)
+                self.num_steps_sampled += batch.count
+                self.buffer_size += batch.count
+                while self.buffer_size > self.max_buffer_size:
+                    evicted = self.replay_buffer.pop(0)
+                    self.buffer_size -= evicted.count
+
+        if self.num_steps_sampled >= self.replay_starts:
+            self._optimize()
+
+    @override(PolicyOptimizer)
+    def stats(self):
+        return dict(
+            PolicyOptimizer.stats(self), **{
+                "sample_time_ms": round(1000 * self.sample_timer.mean, 3),
+                "grad_time_ms": round(1000 * self.grad_timer.mean, 3),
+                "update_time_ms": round(1000 * self.update_weights_timer.mean,
+                                        3),
+                "opt_peak_throughput": round(self.grad_timer.mean_throughput,
+                                             3),
+                "opt_samples": round(self.grad_timer.mean_units_processed, 3),
+                "learner": self.learner_stats,
+            })
+
+    def _optimize(self):
+        samples = [random.choice(self.replay_buffer)]
+        while sum(s.count for s in samples) < self.train_batch_size:
+            samples.append(random.choice(self.replay_buffer))
+        samples = SampleBatch.concat_samples(samples)
+        with self.grad_timer:
+            info_dict = self.local_evaluator.compute_apply(samples)
+            for policy_id, info in info_dict.items():
+                if "stats" in info:
+                    self.learner_stats[policy_id] = info["stats"]
+            self.grad_timer.push_units_processed(samples.count)
+        self.num_steps_trained += samples.count

python/ray/rllib/optimizers/sync_replay_optimizer.py

@@ -13,7 +13,6 @@ from ray.rllib.evaluation.sample_batch import SampleBatch, DEFAULT_POLICY_ID, \
     MultiAgentBatch
 from ray.rllib.utils.annotations import override
 from ray.rllib.utils.compression import pack_if_needed
-from ray.rllib.utils.filter import RunningStat
 from ray.rllib.utils.timer import TimerStat
 from ray.rllib.utils.schedules import LinearSchedule
 
@@ -54,7 +53,6 @@ class SyncReplayOptimizer(PolicyOptimizer):
         self.sample_timer = TimerStat()
         self.replay_timer = TimerStat()
         self.grad_timer = TimerStat()
-        self.throughput = RunningStat()
         self.learner_stats = {}
 
         # Set up replay buffer
@@ -159,13 +157,13 @@ class SyncReplayOptimizer(PolicyOptimizer):
                      dones) = replay_buffer.sample(self.train_batch_size)
                     weights = np.ones_like(rewards)
                     batch_indexes = -np.ones_like(rewards)
-            samples[policy_id] = SampleBatch({
-                "obs": obses_t,
-                "actions": actions,
-                "rewards": rewards,
-                "new_obs": obses_tp1,
-                "dones": dones,
-                "weights": weights,
-                "batch_indexes": batch_indexes
-            })
+                samples[policy_id] = SampleBatch({
+                    "obs": obses_t,
+                    "actions": actions,
+                    "rewards": rewards,
+                    "new_obs": obses_tp1,
+                    "dones": dones,
+                    "weights": weights,
+                    "batch_indexes": batch_indexes
+                })
         return MultiAgentBatch(samples, self.train_batch_size)

python/ray/rllib/test/test_avail_actions_qmix.py

@@ -0,0 +1,68 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from gym.spaces import Tuple, Discrete, Dict, Box
+
+import ray
+from ray.tune import register_env
+from ray.rllib.env.multi_agent_env import MultiAgentEnv
+from ray.rllib.agents.qmix import QMixAgent
+
+
+class AvailActionsTestEnv(MultiAgentEnv):
+    action_space = Discrete(10)
+    observation_space = Dict({
+        "obs": Discrete(3),
+        "action_mask": Box(0, 1, (10, )),
+    })
+
+    def __init__(self, env_config):
+        self.state = None
+        self.avail = env_config["avail_action"]
+        self.action_mask = [0] * 10
+        self.action_mask[env_config["avail_action"]] = 1
+
+    def reset(self):
+        self.state = 0
+        return {
+            "agent_1": {
+                "obs": self.state,
+                "action_mask": self.action_mask
+            }
+        }
+
+    def step(self, action_dict):
+        if self.state > 0:
+            assert action_dict["agent_1"] == self.avail, \
+                "Failed to obey available actions mask!"
+        self.state += 1
+        rewards = {"agent_1": 1}
+        obs = {"agent_1": {"obs": 0, "action_mask": self.action_mask}}
+        dones = {"__all__": self.state > 20}
+        return obs, rewards, dones, {}
+
+
+if __name__ == "__main__":
+    grouping = {
+        "group_1": ["agent_1"],  # trivial grouping for testing
+    }
+    obs_space = Tuple([AvailActionsTestEnv.observation_space])
+    act_space = Tuple([AvailActionsTestEnv.action_space])
+    register_env(
+        "action_mask_test",
+        lambda config: AvailActionsTestEnv(config).with_agent_groups(
+            grouping, obs_space=obs_space, act_space=act_space))
+
+    ray.init()
+    agent = QMixAgent(
+        env="action_mask_test",
+        config={
+            "num_envs_per_worker": 5,  # test with vectorization on
+            "env_config": {
+                "avail_action": 3,
+            },
+        })
+    for _ in range(5):
+        agent.train()  # OK if it doesn't trip the action assertion error
+    assert agent.train()["episode_reward_mean"] == 21.0

python/ray/rllib/test/test_multi_agent_env.py

@@ -339,7 +339,8 @@ class TestMultiAgentEnv(unittest.TestCase):
                                 state_batches,
                                 prev_action_batch=None,
                                 prev_reward_batch=None,
-                                episodes=None):
+                                episodes=None,
+                                **kwargs):
                 return [0] * len(obs_batch), [[h] * len(obs_batch)], {}
 
             def get_initial_state(self):
@@ -363,7 +364,8 @@ class TestMultiAgentEnv(unittest.TestCase):
                                 state_batches,
                                 prev_action_batch=None,
                                 prev_reward_batch=None,
-                                episodes=None):
+                                episodes=None,
+                                **kwargs):
                 # Pretend we did a model-based rollout and want to return
                 # the extra trajectory.
                 builder = episodes[0].new_batch_builder()

python/ray/rllib/test/test_policy_evaluator.py

@@ -25,7 +25,8 @@ class MockPolicyGraph(PolicyGraph):
                         state_batches,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        episodes=None):
+                        episodes=None,
+                        **kwargs):
         return [0] * len(obs_batch), [], {}
 
     def postprocess_trajectory(self,
@@ -42,7 +43,8 @@ class BadPolicyGraph(PolicyGraph):
                         state_batches,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        episodes=None):
+                        episodes=None,
+                        **kwargs):
         raise Exception("intentional error")
 
     def postprocess_trajectory(self,

test/jenkins_tests/run_multi_node_tests.sh

@@ -370,6 +370,9 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
 
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/rllib/test/test_avail_actions_qmix.py
+
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
 
@@ -382,6 +385,15 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
 
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
+
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
         --batch-size=1 --strategy=simple