[rllib] Properly flatten 2-d observations as input to FCnet (#5733)

rllib/models/catalog.py

@@ -450,7 +450,7 @@ class ModelCatalog(object):
         else:
             obs_rank = len(obs_space.shape)
 
-        if obs_rank > 1:
+        if obs_rank > 2:
             return PyTorchVisionNet(obs_space, action_space, num_outputs,
                                     model_config, name)
 
@@ -506,7 +506,7 @@ class ModelCatalog(object):
 
         obs_rank = len(input_dict["obs"].shape) - 1
 
-        if obs_rank > 1:
+        if obs_rank > 2:
             return VisionNetwork(input_dict, obs_space, action_space,
                                  num_outputs, options)
 
@@ -521,7 +521,7 @@ class ModelCatalog(object):
         if options.get("use_lstm"):
             return None  # TODO: default LSTM v2 not implemented
 
-        if obs_rank > 1:
+        if obs_rank > 2:
             return VisionNetV2
 
         return FCNetV2

rllib/models/model.py

@@ -190,6 +190,18 @@ class Model(object):
                         self._num_outputs, shape))
 
 
+@DeveloperAPI
+def flatten(obs, framework):
+    """Flatten the given tensor."""
+    if framework == "tf":
+        return tf.layers.flatten(obs)
+    elif framework == "torch":
+        import torch
+        return torch.flatten(obs, start_dim=1)
+    else:
+        raise NotImplementedError("flatten", framework)
+
+
 @DeveloperAPI
 def restore_original_dimensions(obs, obs_space, tensorlib=tf):
     """Unpacks Dict and Tuple space observations into their original form.

rllib/models/modelv2.py

@@ -3,7 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 from ray.rllib.policy.sample_batch import SampleBatch
-from ray.rllib.models.model import restore_original_dimensions
+from ray.rllib.models.model import restore_original_dimensions, flatten
 from ray.rllib.utils.annotations import PublicAPI
 
 
@@ -146,7 +146,10 @@ class ModelV2(object):
         restored = input_dict.copy()
         restored["obs"] = restore_original_dimensions(
             input_dict["obs"], self.obs_space, self.framework)
-        restored["obs_flat"] = input_dict["obs"]
+        if len(input_dict["obs"].shape) > 2:
+            restored["obs_flat"] = flatten(input_dict["obs"], self.framework)
+        else:
+            restored["obs_flat"] = input_dict["obs"]
         with self.context():
             res = self.forward(restored, state or [], seq_lens)
         if ((not isinstance(res, list) and not isinstance(res, tuple))

rllib/models/tf/fcnet_v1.py

@@ -25,6 +25,9 @@ class FullyConnectedNetwork(Model):
         hiddens = options.get("fcnet_hiddens")
         activation = get_activation_fn(options.get("fcnet_activation"))
 
+        if len(inputs.shape) > 2:
+            inputs = tf.layers.flatten(inputs)
+
         with tf.name_scope("fc_net"):
             i = 1
             last_layer = inputs

rllib/models/tf/fcnet_v2.py

@@ -2,6 +2,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+
 from ray.rllib.models.tf.tf_modelv2 import TFModelV2
 from ray.rllib.models.tf.misc import normc_initializer, get_activation_fn
 from ray.rllib.utils import try_import_tf
@@ -22,8 +24,9 @@ class FullyConnectedNetwork(TFModelV2):
         no_final_linear = model_config.get("no_final_linear")
         vf_share_layers = model_config.get("vf_share_layers")
 
+        # we are using obs_flat, so take the flattened shape as input
         inputs = tf.keras.layers.Input(
-            shape=obs_space.shape, name="observations")
+            shape=(np.product(obs_space.shape), ), name="observations")
         last_layer = inputs
         i = 1
 

rllib/tests/test_supported_spaces.py

@@ -31,6 +31,7 @@ ACTION_SPACES_TO_TEST = {
 OBSERVATION_SPACES_TO_TEST = {
     "discrete": Discrete(5),
     "vector": Box(-1.0, 1.0, (5, ), dtype=np.float32),
+    "vector2": Box(-1.0, 1.0, (5, 5), dtype=np.float32),
     "image": Box(-1.0, 1.0, (84, 84, 1), dtype=np.float32),
     "atari": Box(-1.0, 1.0, (210, 160, 3), dtype=np.float32),
     "tuple": Tuple([Discrete(10),
@@ -106,6 +107,7 @@ def check_support(alg, config, stats, check_bounds=False, name=None):
 def check_support_multiagent(alg, config):
     register_env("multi_mountaincar", lambda _: MultiMountainCar(2))
     register_env("multi_cartpole", lambda _: MultiCartpole(2))
+    config["log_level"] = "ERROR"
     if "DDPG" in alg:
         a = get_agent_class(alg)(config=config, env="multi_mountaincar")
     else: