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[RLLib] MAML extension for all models except RNNs (#11337)

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This commit is contained in:
Michael Luo
2020-11-12 16:51:40 -08:00
committed by GitHub
parent 272edcca94
commit 59bc1e6c09
4 changed files with 94 additions and 164 deletions
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rllib/BUILD
+8 -6
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@@ -234,6 +234,7 @@ py_test(
)
# Working, but takes a long time to learn (>15min).
# Removed due to Higher API conflicts with Pytorch-Import tests
## MB-MPO
#py_test(
# name = "run_regression_tests_pendulum_mbmpo_torch",
@@ -510,12 +511,13 @@ py_test(
)
# MBMPOTrainer
py_test(
name = "test_mbmpo",
tags = ["agents_dir"],
size = "medium",
srcs = ["agents/mbmpo/tests/test_mbmpo.py"]
)
# Removed due to Higher API conflicts with Pytorch-Import tests
#py_test(
# name = "test_mbmpo",
# tags = ["agents_dir"],
# size = "medium",
# srcs = ["agents/mbmpo/tests/test_mbmpo.py"]
#)
# PGTrainer
py_test(
rllib/agents/maml/maml_torch_policy.py
+83 -156
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@@ -9,7 +9,6 @@ from ray.rllib.agents.ppo.ppo_tf_policy import postprocess_ppo_gae, \
from ray.rllib.agents.ppo.ppo_torch_policy import vf_preds_fetches, \
ValueNetworkMixin
from ray.rllib.agents.a3c.a3c_torch_policy import apply_grad_clipping
from ray.rllib.utils.framework import get_activation_fn
from ray.rllib.utils.framework import try_import_torch
torch, nn = try_import_torch()
@@ -127,6 +126,7 @@ class MAMLLoss(object):
obs,
num_tasks,
split,
meta_opt,
inner_adaptation_steps=1,
entropy_coeff=0,
clip_param=0.3,
@@ -134,12 +134,17 @@ class MAMLLoss(object):
vf_loss_coeff=1.0,
use_gae=True):
import higher
self.config = config
self.num_tasks = num_tasks
self.inner_adaptation_steps = inner_adaptation_steps
self.clip_param = clip_param
self.dist_class = dist_class
self.cur_kl_coeff = cur_kl_coeff
self.model = model
self.vf_clip_param = vf_clip_param
self.vf_loss_coeff = vf_loss_coeff
self.entropy_coeff = entropy_coeff
# Split episode tensors into [inner_adaptation_steps+1, num_tasks, -1]
self.obs = self.split_placeholders(obs, split)
@@ -150,166 +155,85 @@ class MAMLLoss(object):
self.value_targets = self.split_placeholders(value_targets, split)
self.vf_preds = self.split_placeholders(vf_preds, split)
# Construct name to tensor dictionary for easier indexing
self.policy_vars = {}
for name, w in policy_vars:
self.policy_vars[name] = w
inner_opt = torch.optim.SGD(model.parameters(), lr=config["inner_lr"])
surr_losses = []
val_losses = []
kl_losses = []
entropy_losses = []
meta_losses = []
kls = []
# Calculate pi_new for PPO
pi_new_logits, current_policy_vars, value_fns = [], [], []
meta_opt.zero_grad()
for i in range(self.num_tasks):
pi_new, value_fn = self.feed_forward(
self.obs[0][i],
self.policy_vars,
policy_config=config["model"])
pi_new_logits.append(pi_new)
value_fns.append(value_fn)
current_policy_vars.append(self.policy_vars)
with higher.innerloop_ctx(
model, inner_opt, copy_initial_weights=False) as (fnet,
diffopt):
inner_kls = []
for step in range(self.inner_adaptation_steps):
ppo_loss, _, inner_kl_loss, _, _ = self.compute_losses(
fnet, step, i)
diffopt.step(ppo_loss)
inner_kls.append(inner_kl_loss)
kls.append(inner_kl_loss.detach())
inner_kls = []
inner_ppo_loss = []
# Meta Update
ppo_loss, s_loss, kl_loss, v_loss, ent = self.compute_losses(
fnet, self.inner_adaptation_steps, i, clip_loss=True)
# Recompute weights for inner-adaptation (same weights as workers)
for step in range(self.inner_adaptation_steps):
kls = []
for i in range(self.num_tasks):
# PPO Loss Function (only Surrogate)
ppo_loss, _, kl_loss, _, _ = PPOLoss(
dist_class=dist_class,
actions=self.actions[step][i],
curr_logits=pi_new_logits[i],
behaviour_logits=self.behaviour_logits[step][i],
advantages=self.advantages[step][i],
value_fn=value_fns[i],
value_targets=self.value_targets[step][i],
vf_preds=self.vf_preds[step][i],
cur_kl_coeff=0.0,
entropy_coeff=entropy_coeff,
clip_param=clip_param,
vf_clip_param=vf_clip_param,
vf_loss_coeff=vf_loss_coeff,
clip_loss=False)
inner_loss = torch.mean(
torch.stack([
a * b for a, b in zip(
self.cur_kl_coeff[
i * self.inner_adaptation_steps:(i + 1) *
self.inner_adaptation_steps], inner_kls)
]))
meta_loss = (ppo_loss + inner_loss) / self.num_tasks
meta_loss.backward()
adapted_policy_vars = self.compute_updated_variables(
ppo_loss, current_policy_vars[i], model)
pi_new_logits[i], value_fns[i] = self.feed_forward(
self.obs[step + 1][i],
adapted_policy_vars,
policy_config=config["model"])
current_policy_vars[i] = adapted_policy_vars
kls.append(kl_loss)
inner_ppo_loss.append(ppo_loss)
inner_kls.extend(kls)
surr_losses.append(s_loss.detach())
kl_losses.append(kl_loss.detach())
val_losses.append(v_loss.detach())
entropy_losses.append(ent.detach())
meta_losses.append(meta_loss.detach())
self.mean_inner_kl = inner_kls
meta_opt.step()
ppo_obj = []
for i in range(self.num_tasks):
ppo_loss, surr_loss, kl_loss, val_loss, entropy_loss = PPOLoss(
dist_class=dist_class,
actions=self.actions[self.inner_adaptation_steps][i],
curr_logits=pi_new_logits[i],
behaviour_logits=self.behaviour_logits[
self.inner_adaptation_steps][i],
advantages=self.advantages[self.inner_adaptation_steps][i],
value_fn=value_fns[i],
value_targets=self.value_targets[self.inner_adaptation_steps][
i],
vf_preds=self.vf_preds[self.inner_adaptation_steps][i],
cur_kl_coeff=0.0,
entropy_coeff=entropy_coeff,
clip_param=clip_param,
vf_clip_param=vf_clip_param,
vf_loss_coeff=vf_loss_coeff,
clip_loss=True)
ppo_obj.append(ppo_loss)
self.mean_policy_loss = surr_loss
self.mean_kl = kl_loss
self.mean_vf_loss = val_loss
self.mean_entropy = entropy_loss
# Stats Logging
self.mean_policy_loss = torch.mean(torch.stack(surr_losses))
self.mean_kl_loss = torch.mean(torch.stack(kl_losses))
self.mean_vf_loss = torch.mean(torch.stack(val_losses))
self.mean_entropy = torch.mean(torch.stack(entropy_losses))
self.mean_inner_kl = kls
self.loss = torch.sum(torch.stack(meta_losses))
# Hacky, needed to bypass RLlib backend
self.loss.requires_grad = True
self.inner_kl_loss = torch.mean(
torch.stack([
a * b for a, b in zip(self.cur_kl_coeff, self.mean_inner_kl)
]))
self.loss = torch.mean(torch.stack(ppo_obj)) + self.inner_kl_loss
def feed_forward(self, obs, policy_vars, policy_config):
# Hacky for now, reconstruct FC network with adapted weights
# @mluo: TODO for any network
def fc_network(inp, network_vars, hidden_nonlinearity,
output_nonlinearity, policy_config, hiddens_name,
logits_name):
x = inp
hidden_w = []
logits_w = []
for name, w in network_vars.items():
if hiddens_name in name:
hidden_w.append(w)
elif logits_name in name:
logits_w.append(w)
else:
raise NameError
assert len(hidden_w) % 2 == 0 and len(logits_w) == 2
while len(hidden_w) != 0:
x = nn.functional.linear(x, hidden_w.pop(0), hidden_w.pop(0))
x = hidden_nonlinearity()(x)
x = nn.functional.linear(x, logits_w.pop(0), logits_w.pop(0))
x = output_nonlinearity()(x)
return x
policyn_vars = {}
valuen_vars = {}
log_std = None
for name, param in policy_vars.items():
if "value" in name:
valuen_vars[name] = param
elif "log_std" in name:
log_std = param
else:
policyn_vars[name] = param
output_nonlinearity = nn.Identity
hidden_nonlinearity = get_activation_fn(
policy_config["fcnet_activation"], framework="torch")
pi_new_logits = fc_network(obs, policyn_vars, hidden_nonlinearity,
output_nonlinearity, policy_config,
"hidden_layers", "logits")
if log_std is not None:
pi_new_logits = torch.cat(
[
pi_new_logits,
log_std.unsqueeze(0).repeat([len(pi_new_logits), 1])
],
axis=1)
value_fn = fc_network(obs, valuen_vars, hidden_nonlinearity,
output_nonlinearity, policy_config,
"value_branch_separate", "value_branch")
return pi_new_logits, torch.squeeze(value_fn)
def compute_updated_variables(self, loss, network_vars, model):
grad = torch.autograd.grad(
loss,
inputs=model.parameters(),
create_graph=True,
allow_unused=True)
adapted_vars = {}
for i, tup in enumerate(network_vars.items()):
name, var = tup
if grad[i] is None:
adapted_vars[name] = var
else:
adapted_vars[name] = var - self.config["inner_lr"] * grad[i]
return adapted_vars
def compute_losses(self,
model,
inner_adapt_iter,
task_iter,
clip_loss=False):
obs = self.obs[inner_adapt_iter][task_iter]
obs_dict = {"obs": obs, "obs_flat": obs}
curr_logits, _ = model.forward(obs_dict, None, None)
value_fns = model.value_function()
ppo_loss, surr_loss, kl_loss, val_loss, ent_loss = PPOLoss(
dist_class=self.dist_class,
actions=self.actions[inner_adapt_iter][task_iter],
curr_logits=curr_logits,
behaviour_logits=self.behaviour_logits[inner_adapt_iter][
task_iter],
advantages=self.advantages[inner_adapt_iter][task_iter],
value_fn=value_fns,
value_targets=self.value_targets[inner_adapt_iter][task_iter],
vf_preds=self.vf_preds[inner_adapt_iter][task_iter],
cur_kl_coeff=0.0,
entropy_coeff=self.entropy_coeff,
clip_param=self.clip_param,
vf_clip_param=self.vf_clip_param,
vf_loss_coeff=self.vf_loss_coeff,
clip_loss=clip_loss)
return ppo_loss, surr_loss, kl_loss, val_loss, ent_loss
def split_placeholders(self, placeholder, split):
inner_placeholder_list = torch.split(
@@ -368,7 +292,8 @@ def maml_loss(policy, model, dist_class, train_batch):
clip_param=policy.config["clip_param"],
vf_clip_param=policy.config["vf_clip_param"],
vf_loss_coeff=policy.config["vf_loss_coeff"],
use_gae=policy.config["use_gae"])
use_gae=policy.config["use_gae"],
meta_opt=policy.meta_opt)
return policy.loss_obj.loss
@@ -383,7 +308,7 @@ def maml_stats(policy, train_batch):
"total_loss": policy.loss_obj.loss,
"policy_loss": policy.loss_obj.mean_policy_loss,
"vf_loss": policy.loss_obj.mean_vf_loss,
"kl": policy.loss_obj.mean_kl,
"kl_loss": policy.loss_obj.mean_kl_loss,
"inner_kl": policy.loss_obj.mean_inner_kl,
"entropy": policy.loss_obj.mean_entropy,
}
@@ -411,7 +336,9 @@ def maml_optimizer_fn(policy, config):
Meta-Policy uses Adam optimizer for meta-update
"""
if not config["worker_index"]:
return torch.optim.Adam(policy.model.parameters(), lr=config["lr"])
policy.meta_opt = torch.optim.Adam(
policy.model.parameters(), lr=config["lr"])
return policy.meta_opt
return torch.optim.SGD(policy.model.parameters(), lr=config["inner_lr"])
rllib/agents/maml/tests/test_maml.py
+1 -1
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@@ -23,7 +23,7 @@ class TestMAML(unittest.TestCase):
num_iterations = 1
# Test for tf framework (torch not implemented yet).
for _ in framework_iterator(config, frameworks=("tf", "torch")):
for _ in framework_iterator(config, frameworks=("tf")):
trainer = maml.MAMLTrainer(
config=config,
env="ray.rllib.examples.env.pendulum_mass.PendulumMassEnv")
rllib/tuned_examples/maml/cartpole-maml.yaml
+2 -1
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@@ -5,6 +5,8 @@ cartpole-maml:
stop:
training_iteration: 100
config:
# Works with both frameworks, "tf" and "torch".
framework: torch
horizon: 200
rollout_fragment_length: 200
num_envs_per_worker: 10
@@ -24,4 +26,3 @@ cartpole-maml:
use_meta_env: False
model:
fcnet_hiddens: [64, 64]
free_log_std: True