def estimate(
self,
batch: SampleBatchType,
) -> OffPolicyEstimate:
self.check_can_estimate_for(batch)
estimates = []
# Split data into train and test batches
for train_episodes, test_episodes in train_test_split(
batch,
self.train_test_split_val,
self.k,
):
# Train Q-function
if train_episodes:
# Reinitialize model
self.model.reset()
train_batch = SampleBatch.concat_samples(train_episodes)
losses = self.train(train_batch)
self.losses.append(losses)
# Calculate doubly robust OPE estimates
for episode in test_episodes:
rewards, old_prob = episode["rewards"], episode["action_prob"]
new_prob = np.exp(self.action_log_likelihood(episode))
v_old = 0.0
v_new = 0.0
q_values = self.model.estimate_q(episode[SampleBatch.OBS],
episode[SampleBatch.ACTIONS])
q_values = convert_to_numpy(q_values)
all_actions = np.zeros(
[episode.count, self.policy.action_space.n])
all_actions[:] = np.arange(self.policy.action_space.n)
# Two transposes required for torch.distributions to work
tmp_episode = episode.copy()
tmp_episode[SampleBatch.ACTIONS] = all_actions.T
action_probs = np.exp(
self.action_log_likelihood(tmp_episode)).T
v_values = self.model.estimate_v(episode[SampleBatch.OBS],
action_probs)
v_values = convert_to_numpy(v_values)
for t in reversed(range(episode.count)):
v_old = rewards[t] + self.gamma * v_old
v_new = v_values[t] + (new_prob[t] / old_prob[t]) * (
rewards[t] + self.gamma * v_new - q_values[t])
v_new = v_new.item()
estimates.append(
OffPolicyEstimate(
self.name,
{
"v_old": v_old,
"v_new": v_new,
"v_gain": v_new / max(1e-8, v_old),
},
))
return estimates
def on_policy_output(self, env_id: str, agent_id: str,
output: PolicyOutputType):
# Buffer latest output states for next input __call__.
action, states, _ = output
agent_state = self._states[env_id][agent_id]
agent_state.action = convert_to_numpy(action)
agent_state.states = convert_to_numpy(states)
def get_state(self, sess: Optional["tf.Session"] = None):
if sess:
return sess.run(self._tf_state_op)
eps = self.epsilon_schedule(self.last_timestep)
return {
"cur_epsilon": convert_to_numpy(eps)
if self.framework != "tf" else eps,
"last_timestep": convert_to_numpy(self.last_timestep)
if self.framework != "tf" else self.last_timestep,
}
def estimate(self, batch: SampleBatchType) -> Dict[str, Any]:
"""Compute off-policy estimates.
Args:
batch: The SampleBatch to run off-policy estimation on
Returns:
A dict consists of the following metrics:
- v_behavior: The discounted return averaged over episodes in the batch
- v_behavior_std: The standard deviation corresponding to v_behavior
- v_target: The estimated discounted return for `self.policy`,
averaged over episodes in the batch
- v_target_std: The standard deviation corresponding to v_target
- v_gain: v_target / max(v_behavior, 1e-8), averaged over episodes
- v_gain_std: The standard deviation corresponding to v_gain
"""
batch = self.convert_ma_batch_to_sample_batch(batch)
self.check_action_prob_in_batch(batch)
estimates = {"v_behavior": [], "v_target": [], "v_gain": []}
# Calculate doubly robust OPE estimates
for episode in batch.split_by_episode():
rewards, old_prob = episode["rewards"], episode["action_prob"]
log_likelihoods = compute_log_likelihoods_from_input_dict(
self.policy, episode
)
new_prob = np.exp(convert_to_numpy(log_likelihoods))
v_behavior = 0.0
v_target = 0.0
q_values = self.model.estimate_q(episode)
q_values = convert_to_numpy(q_values)
v_values = self.model.estimate_v(episode)
v_values = convert_to_numpy(v_values)
assert q_values.shape == v_values.shape == (episode.count,)
for t in reversed(range(episode.count)):
v_behavior = rewards[t] + self.gamma * v_behavior
v_target = v_values[t] + (new_prob[t] / old_prob[t]) * (
rewards[t] + self.gamma * v_target - q_values[t]
)
v_target = v_target.item()
estimates["v_behavior"].append(v_behavior)
estimates["v_target"].append(v_target)
estimates["v_gain"].append(v_target / max(v_behavior, 1e-8))
estimates["v_behavior_std"] = np.std(estimates["v_behavior"])
estimates["v_behavior"] = np.mean(estimates["v_behavior"])
estimates["v_target_std"] = np.std(estimates["v_target"])
estimates["v_target"] = np.mean(estimates["v_target"])
estimates["v_gain_std"] = np.std(estimates["v_gain"])
estimates["v_gain"] = np.mean(estimates["v_gain"])
return estimates
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
if self.config["worker_index"]:
return convert_to_numpy({"worker_loss": self.loss_obj.loss})
else:
return convert_to_numpy({
"cur_kl_coeff": self.kl_coeff_val,
"cur_lr": self.cur_lr,
"total_loss": self.loss_obj.loss,
"policy_loss": self.loss_obj.mean_policy_loss,
"vf_loss": self.loss_obj.mean_vf_loss,
"kl_loss": self.loss_obj.mean_kl_loss,
"inner_kl": self.loss_obj.mean_inner_kl,
"entropy": self.loss_obj.mean_entropy,
})
def postprocess_nstep_and_prio(
policy: Policy, batch: SampleBatch, other_agent=None, episode=None
) -> SampleBatch:
# N-step Q adjustments.
if policy.config["n_step"] > 1:
adjust_nstep(policy.config["n_step"], policy.config["gamma"], batch)
# Create dummy prio-weights (1.0) in case we don't have any in
# the batch.
if PRIO_WEIGHTS not in batch:
batch[PRIO_WEIGHTS] = np.ones_like(batch[SampleBatch.REWARDS])
# Prioritize on the worker side.
if batch.count > 0 and policy.config["replay_buffer_config"].get(
"worker_side_prioritization", False
):
td_errors = policy.compute_td_error(
batch[SampleBatch.OBS],
batch[SampleBatch.ACTIONS],
batch[SampleBatch.REWARDS],
batch[SampleBatch.NEXT_OBS],
batch[SampleBatch.DONES],
batch[PRIO_WEIGHTS],
)
# Retain compatibility with old-style Replay args
epsilon = policy.config.get("replay_buffer_config", {}).get(
"prioritized_replay_eps"
) or policy.config.get("prioritized_replay_eps")
if epsilon is None:
raise ValueError("prioritized_replay_eps not defined in config.")
new_priorities = np.abs(convert_to_numpy(td_errors)) + epsilon
batch[PRIO_WEIGHTS] = new_priorities
return batch
def postprocess_nstep_and_prio(policy: Policy,
batch: SampleBatch,
other_agent=None,
episode=None) -> SampleBatch:
# N-step Q adjustments.
if policy.config["n_step"] > 1:
_adjust_nstep(policy.config["n_step"], policy.config["gamma"],
batch[SampleBatch.CUR_OBS], batch[SampleBatch.ACTIONS],
batch[SampleBatch.REWARDS], batch[SampleBatch.NEXT_OBS],
batch[SampleBatch.DONES])
if PRIO_WEIGHTS not in batch:
batch[PRIO_WEIGHTS] = np.ones_like(batch[SampleBatch.REWARDS])
# Prioritize on the worker side.
if batch.count > 0 and policy.config["worker_side_prioritization"]:
td_errors = policy.compute_td_error(batch[SampleBatch.CUR_OBS],
batch[SampleBatch.ACTIONS],
batch[SampleBatch.REWARDS],
batch[SampleBatch.NEXT_OBS],
batch[SampleBatch.DONES],
batch[PRIO_WEIGHTS])
new_priorities = (np.abs(convert_to_numpy(td_errors)) +
policy.config["prioritized_replay_eps"])
batch[PRIO_WEIGHTS] = new_priorities
return batch
def extra_compute_grad_fetches(self):
if extra_learn_fetches_fn:
fetches = convert_to_numpy(extra_learn_fetches_fn(self))
# Auto-add empty learner stats dict if needed.
return dict({LEARNER_STATS_KEY: {}}, **fetches)
else:
return parent_cls.extra_compute_grad_fetches(self)
def centralized_critic_postprocessing(
policy, sample_batch, other_agent_batches=None, episode=None
):
pytorch = policy.config["framework"] == "torch"
if (pytorch and hasattr(policy, "compute_central_vf")) or (
not pytorch and policy.loss_initialized()
):
assert other_agent_batches is not None
[(_, opponent_batch)] = list(other_agent_batches.values())
# also record the opponent obs and actions in the trajectory
sample_batch[OPPONENT_OBS] = opponent_batch[SampleBatch.CUR_OBS]
sample_batch[OPPONENT_ACTION] = opponent_batch[SampleBatch.ACTIONS]
# overwrite default VF prediction with the central VF
if args.framework == "torch":
sample_batch[SampleBatch.VF_PREDS] = (
policy.compute_central_vf(
convert_to_torch_tensor(
sample_batch[SampleBatch.CUR_OBS], policy.device
),
convert_to_torch_tensor(sample_batch[OPPONENT_OBS], policy.device),
convert_to_torch_tensor(
sample_batch[OPPONENT_ACTION], policy.device
),
)
.cpu()
.detach()
.numpy()
)
else:
sample_batch[SampleBatch.VF_PREDS] = convert_to_numpy(
policy.compute_central_vf(
sample_batch[SampleBatch.CUR_OBS],
sample_batch[OPPONENT_OBS],
sample_batch[OPPONENT_ACTION],
)
)
else:
# Policy hasn't been initialized yet, use zeros.
sample_batch[OPPONENT_OBS] = np.zeros_like(sample_batch[SampleBatch.CUR_OBS])
sample_batch[OPPONENT_ACTION] = np.zeros_like(sample_batch[SampleBatch.ACTIONS])
sample_batch[SampleBatch.VF_PREDS] = np.zeros_like(
sample_batch[SampleBatch.REWARDS], dtype=np.float32
)
completed = sample_batch["dones"][-1]
if completed:
last_r = 0.0
else:
last_r = sample_batch[SampleBatch.VF_PREDS][-1]
train_batch = compute_advantages(
sample_batch,
last_r,
policy.config["gamma"],
policy.config["lambda"],
use_gae=policy.config["use_gae"],
)
return train_batch
def learn_on_batch(self, postprocessed_batch):
# Callback handling.
learn_stats = {}
self.callbacks.on_learn_on_batch(
policy=self, train_batch=postprocessed_batch, result=learn_stats
)
pad_batch_to_sequences_of_same_size(
postprocessed_batch,
max_seq_len=self._max_seq_len,
shuffle=False,
batch_divisibility_req=self.batch_divisibility_req,
view_requirements=self.view_requirements,
)
self._is_training = True
postprocessed_batch = self._lazy_tensor_dict(postprocessed_batch)
postprocessed_batch.set_training(True)
stats = self._learn_on_batch_helper(postprocessed_batch)
stats.update(
{
"custom_metrics": learn_stats,
NUM_AGENT_STEPS_TRAINED: postprocessed_batch.count,
}
)
return convert_to_numpy(stats)
def get_state(self, sess: Optional["tf.Session"] = None):
"""Returns the current scale value.
Returns:
Union[float,tf.Tensor[float]]: The current scale value.
"""
if sess:
return sess.run(
dict(
self._tf_state_op,
**{
"ou_state": self.ou_state,
}
)
)
state = super().get_state()
return dict(
state,
**{
"ou_state": convert_to_numpy(self.ou_state)
if self.framework != "tf"
else self.ou_state,
}
)
def get_state(self, sess: Optional["tf.Session"] = None):
"""Returns the current scale value.
Returns:
Union[float,tf.Tensor[float]]: The current scale value.
"""
if sess:
return sess.run(self._tf_state_op)
scale = self.scale_schedule(self.last_timestep)
return {
"cur_scale":
convert_to_numpy(scale) if self.framework != "tf" else scale,
"last_timestep":
convert_to_numpy(self.last_timestep)
if self.framework != "tf" else self.last_timestep,
}
def action_log_likelihood(self, batch: SampleBatchType) -> TensorType:
"""Returns log likelihood for actions in given batch for policy.
Computes likelihoods by passing the observations through the current
policy's `compute_log_likelihoods()` method
Args:
batch: The SampleBatch or MultiAgentBatch to calculate action
log likelihoods from. This batch/batches must contain OBS
and ACTIONS keys.
Returns:
The probabilities of the actions in the batch, given the
observations and the policy.
"""
num_state_inputs = 0
for k in batch.keys():
if k.startswith("state_in_"):
num_state_inputs += 1
state_keys = ["state_in_{}".format(i) for i in range(num_state_inputs)]
log_likelihoods: TensorType = self.policy.compute_log_likelihoods(
actions=batch[SampleBatch.ACTIONS],
obs_batch=batch[SampleBatch.OBS],
state_batches=[batch[k] for k in state_keys],
prev_action_batch=batch.get(SampleBatch.PREV_ACTIONS),
prev_reward_batch=batch.get(SampleBatch.PREV_REWARDS),
actions_normalized=True,
)
log_likelihoods = convert_to_numpy(log_likelihoods)
return log_likelihoods
def estimate(self, batch: SampleBatchType) -> Dict[str, Any]:
"""Compute off-policy estimates.
Args:
batch: The SampleBatch to run off-policy estimation on
Returns:
A dict consists of the following metrics:
- v_behavior: The discounted return averaged over episodes in the batch
- v_behavior_std: The standard deviation corresponding to v_behavior
- v_target: The estimated discounted return for `self.policy`,
averaged over episodes in the batch
- v_target_std: The standard deviation corresponding to v_target
- v_gain: v_target / max(v_behavior, 1e-8), averaged over episodes
- v_gain_std: The standard deviation corresponding to v_gain
"""
batch = self.convert_ma_batch_to_sample_batch(batch)
self.check_action_prob_in_batch(batch)
estimates = {"v_behavior": [], "v_target": [], "v_gain": []}
for episode in batch.split_by_episode():
rewards, old_prob = episode["rewards"], episode["action_prob"]
log_likelihoods = compute_log_likelihoods_from_input_dict(
self.policy, episode)
new_prob = np.exp(convert_to_numpy(log_likelihoods))
# calculate importance ratios
p = []
for t in range(episode.count):
if t == 0:
pt_prev = 1.0
else:
pt_prev = p[t - 1]
p.append(pt_prev * new_prob[t] / old_prob[t])
for t, v in enumerate(p):
if t >= len(self.filter_values):
self.filter_values.append(v)
self.filter_counts.append(1.0)
else:
self.filter_values[t] += v
self.filter_counts[t] += 1.0
# calculate stepwise weighted IS estimate
v_behavior = 0.0
v_target = 0.0
for t in range(episode.count):
v_behavior += rewards[t] * self.gamma**t
w_t = self.filter_values[t] / self.filter_counts[t]
v_target += p[t] / w_t * rewards[t] * self.gamma**t
estimates["v_behavior"].append(v_behavior)
estimates["v_target"].append(v_target)
estimates["v_gain"].append(v_target / max(v_behavior, 1e-8))
estimates["v_behavior_std"] = np.std(estimates["v_behavior"])
estimates["v_behavior"] = np.mean(estimates["v_behavior"])
estimates["v_target_std"] = np.std(estimates["v_target"])
estimates["v_target"] = np.mean(estimates["v_target"])
estimates["v_gain_std"] = np.std(estimates["v_gain"])
estimates["v_gain"] = np.mean(estimates["v_gain"])
return estimates
def get_state(self) -> Union[Dict[str, TensorType], List[TensorType]]:
state = super().get_state()
state["_optimizer_variables"] = []
for i, o in enumerate(self._optimizers):
optim_state_dict = convert_to_numpy(o.state_dict())
state["_optimizer_variables"].append(optim_state_dict)
# Add exploration state.
state["_exploration_state"] = self.exploration.get_state()
return state
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
q_t = torch.stack(self.get_tower_stats("q_t"))
stats = {
"actor_loss": torch.mean(torch.stack(self.get_tower_stats("actor_loss"))),
"critic_loss": torch.mean(torch.stack(self.get_tower_stats("critic_loss"))),
"mean_q": torch.mean(q_t),
"max_q": torch.max(q_t),
"min_q": torch.min(q_t),
}
return convert_to_numpy(stats)
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
stats = {
"policy_loss": self.p_loss,
"total_loss": self.total_loss,
}
if self.config["beta"] != 0.0:
stats[
"moving_average_sqd_adv_norm"] = self._moving_average_sqd_adv_norm
stats["vf_explained_var"] = self.explained_variance
stats["vf_loss"] = self.v_loss
return convert_to_numpy(stats)
def test_fqe_model(self):
# Test FQETorchModel for:
# (1) Check that it does not modify the underlying batch during training
# (2) Check that the stoppign criteria from FQE are working correctly
# (3) Check that using fqe._compute_action_probs equals brute force
# iterating over all actions with policy.compute_log_likelihoods
fqe = FQETorchModel(
policy=self.algo.get_policy(),
gamma=self.gamma,
**self.q_model_config,
)
tmp_batch = copy.deepcopy(self.batch)
losses = fqe.train(self.batch)
# Make sure FQETorchModel.train() does not modify self.batch
check(tmp_batch, self.batch)
# Make sure FQE stopping criteria are respected
assert (
len(losses) == fqe.n_iters or losses[-1] < fqe.delta
), f"FQE.train() terminated early in {len(losses)} steps with final loss"
f"{losses[-1]} for n_iters: {fqe.n_iters} and delta: {fqe.delta}"
# Test fqe._compute_action_probs against "brute force" method
# of computing log_prob for each possible action individually
# using policy.compute_log_likelihoods
obs = torch.tensor(self.batch["obs"], device=fqe.device)
action_probs = fqe._compute_action_probs(obs)
action_probs = convert_to_numpy(action_probs)
tmp_probs = []
for act in range(fqe.policy.action_space.n):
tmp_actions = np.zeros_like(self.batch["actions"]) + act
log_probs = fqe.policy.compute_log_likelihoods(
actions=tmp_actions,
obs_batch=self.batch["obs"],
)
tmp_probs.append(torch.exp(log_probs))
tmp_probs = torch.stack(tmp_probs).transpose(0, 1)
tmp_probs = convert_to_numpy(tmp_probs)
check(action_probs, tmp_probs, decimals=3)
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
return convert_to_numpy({
"cur_lr":
self.cur_lr,
"entropy_coeff":
self.entropy_coeff,
"policy_entropy":
torch.mean(torch.stack(self.get_tower_stats("entropy"))),
"policy_loss":
torch.mean(torch.stack(self.get_tower_stats("pi_err"))),
"vf_loss":
torch.mean(torch.stack(self.get_tower_stats("value_err"))),
})
def action_prob(self, batch: SampleBatchType) -> np.ndarray:
"""Returns the probs for the batch actions for the current policy."""
num_state_inputs = 0
for k in batch.keys():
if k.startswith("state_in_"):
num_state_inputs += 1
state_keys = ["state_in_{}".format(i) for i in range(num_state_inputs)]
log_likelihoods: TensorType = self.policy.compute_log_likelihoods(
actions=batch[SampleBatch.ACTIONS],
obs_batch=batch[SampleBatch.CUR_OBS],
state_batches=[batch[k] for k in state_keys],
prev_action_batch=batch.data.get(SampleBatch.PREV_ACTIONS),
prev_reward_batch=batch.data.get(SampleBatch.PREV_REWARDS))
return convert_to_numpy(log_likelihoods)
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
"""Returns the calculated loss in a stats dict.
Args:
policy: The Policy object.
train_batch: The data used for training.
Returns:
Dict[str, TensorType]: The stats dict.
"""
return convert_to_numpy({
"policy_loss":
torch.mean(torch.stack(self.get_tower_stats("policy_loss"))),
})
def compute_actions_from_input_dict(
self,
input_dict: Dict[str, TensorType],
explore: bool = None,
timestep: Optional[int] = None,
episodes: Optional[List[Episode]] = None,
**kwargs,
) -> Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
if not self.config.get(
"eager_tracing") and not tf1.executing_eagerly():
tf1.enable_eager_execution()
self._is_training = False
explore = explore if explore is not None else self.explore
timestep = timestep if timestep is not None else self.global_timestep
if isinstance(timestep, tf.Tensor):
timestep = int(timestep.numpy())
# Pass lazy (eager) tensor dict to Model as `input_dict`.
input_dict = self._lazy_tensor_dict(input_dict)
input_dict.set_training(False)
# Pack internal state inputs into (separate) list.
state_batches = [
input_dict[k] for k in input_dict.keys() if "state_in" in k[:8]
]
self._state_in = state_batches
self._is_recurrent = state_batches != []
# Call the exploration before_compute_actions hook.
self.exploration.before_compute_actions(timestep=timestep,
explore=explore,
tf_sess=self.get_session())
ret = self._compute_actions_helper(
input_dict,
state_batches,
# TODO: Passing episodes into a traced method does not work.
None if self.config["eager_tracing"] else episodes,
explore,
timestep,
)
# Update our global timestep by the batch size.
self.global_timestep.assign_add(
tree.flatten(ret[0])[0].shape.as_list()[0])
return convert_to_numpy(ret)
def compute_gradients(self, postprocessed_batch: SampleBatch) -> \
Tuple[ModelGradients, Dict[str, TensorType]]:
pad_batch_to_sequences_of_same_size(
postprocessed_batch,
shuffle=False,
max_seq_len=self._max_seq_len,
batch_divisibility_req=self.batch_divisibility_req,
view_requirements=self.view_requirements,
)
self._is_training = True
self._lazy_tensor_dict(postprocessed_batch)
postprocessed_batch.set_training(True)
grads_and_vars, grads, stats = self._compute_gradients_helper(
postprocessed_batch)
return convert_to_numpy((grads, stats))
def estimate(self, batch: SampleBatchType) -> OffPolicyEstimate:
self.check_can_estimate_for(batch)
estimates = []
# Split data into train and test batches
for train_episodes, test_episodes in train_test_split(
batch,
self.train_test_split_val,
self.k,
):
# Train Q-function
if train_episodes:
# Reinitialize model
self.model.reset()
train_batch = SampleBatch.concat_samples(train_episodes)
losses = self.train(train_batch)
self.losses.append(losses)
# Calculate direct method OPE estimates
for episode in test_episodes:
rewards = episode["rewards"]
v_old = 0.0
v_new = 0.0
for t in range(episode.count):
v_old += rewards[t] * self.gamma ** t
init_step = episode[0:1]
init_obs = np.array([init_step[SampleBatch.OBS]])
all_actions = np.arange(self.policy.action_space.n, dtype=float)
init_step[SampleBatch.ACTIONS] = all_actions
action_probs = np.exp(self.action_log_likelihood(init_step))
v_value = self.model.estimate_v(init_obs, action_probs)
v_new = convert_to_numpy(v_value).item()
estimates.append(
OffPolicyEstimate(
self.name,
{
"v_old": v_old,
"v_new": v_new,
"v_gain": v_new / max(1e-8, v_old),
},
)
)
return estimates
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
return convert_to_numpy({
"cur_lr":
self.cur_lr,
"total_loss":
torch.mean(torch.stack(self.get_tower_stats("total_loss"))),
"policy_loss":
torch.mean(torch.stack(self.get_tower_stats("pi_loss"))),
"entropy":
torch.mean(torch.stack(self.get_tower_stats("mean_entropy"))),
"entropy_coeff":
self.entropy_coeff,
"var_gnorm":
global_norm(self.model.trainable_variables()),
"vf_loss":
torch.mean(torch.stack(self.get_tower_stats("vf_loss"))),
"vf_explained_var":
torch.mean(torch.stack(self.get_tower_stats("vf_explained_var"))),
})
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
"""Stats function for APPO. Returns a dict with important loss stats.
Args:
policy: The Policy to generate stats for.
train_batch: The SampleBatch (already) used for training.
Returns:
Dict[str, TensorType]: The stats dict.
"""
stats_dict = {
"cur_lr":
self.cur_lr,
"total_loss":
torch.mean(torch.stack(self.get_tower_stats("total_loss"))),
"policy_loss":
torch.mean(torch.stack(self.get_tower_stats("mean_policy_loss"))),
"entropy":
torch.mean(torch.stack(self.get_tower_stats("mean_entropy"))),
"entropy_coeff":
self.entropy_coeff,
"var_gnorm":
global_norm(self.model.trainable_variables()),
"vf_loss":
torch.mean(torch.stack(self.get_tower_stats("mean_vf_loss"))),
"vf_explained_var":
torch.mean(torch.stack(self.get_tower_stats("vf_explained_var"))),
}
if self.config["vtrace"]:
is_stat_mean = torch.mean(self._is_ratio, [0, 1])
is_stat_var = torch.var(self._is_ratio, [0, 1])
stats_dict["mean_IS"] = is_stat_mean
stats_dict["var_IS"] = is_stat_var
if self.config["use_kl_loss"]:
stats_dict["kl"] = torch.mean(
torch.stack(self.get_tower_stats("mean_kl_loss")))
stats_dict["KL_Coeff"] = self.kl_coeff
return convert_to_numpy(stats_dict)
def estimate(self, batch: SampleBatchType) -> Dict[str, Any]:
"""Compute off-policy estimates.
Args:
batch: The SampleBatch to run off-policy estimation on
Returns:
A dict consists of the following metrics:
- v_behavior: The discounted return averaged over episodes in the batch
- v_behavior_std: The standard deviation corresponding to v_behavior
- v_target: The estimated discounted return for `self.policy`,
averaged over episodes in the batch
- v_target_std: The standard deviation corresponding to v_target
- v_gain: v_target / max(v_behavior, 1e-8), averaged over episodes
- v_gain_std: The standard deviation corresponding to v_gain
"""
batch = self.convert_ma_batch_to_sample_batch(batch)
self.check_action_prob_in_batch(batch)
estimates = {"v_behavior": [], "v_target": [], "v_gain": []}
# Calculate Direct Method OPE estimates
for episode in batch.split_by_episode():
rewards = episode["rewards"]
v_behavior = 0.0
v_target = 0.0
for t in range(episode.count):
v_behavior += rewards[t] * self.gamma ** t
init_step = episode[0:1]
v_target = self.model.estimate_v(init_step)
v_target = convert_to_numpy(v_target).item()
estimates["v_behavior"].append(v_behavior)
estimates["v_target"].append(v_target)
estimates["v_gain"].append(v_target / max(v_behavior, 1e-8))
estimates["v_behavior_std"] = np.std(estimates["v_behavior"])
estimates["v_behavior"] = np.mean(estimates["v_behavior"])
estimates["v_target_std"] = np.std(estimates["v_target"])
estimates["v_target"] = np.mean(estimates["v_target"])
estimates["v_gain_std"] = np.std(estimates["v_gain"])
estimates["v_gain"] = np.mean(estimates["v_gain"])
return estimates
def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
return convert_to_numpy({
"cur_kl_coeff":
self.kl_coeff,
"cur_lr":
self.cur_lr,
"total_loss":
torch.mean(torch.stack(self.get_tower_stats("total_loss"))),
"policy_loss":
torch.mean(torch.stack(self.get_tower_stats("mean_policy_loss"))),
"vf_loss":
torch.mean(torch.stack(self.get_tower_stats("mean_vf_loss"))),
"vf_explained_var":
torch.mean(torch.stack(self.get_tower_stats("vf_explained_var"))),
"kl":
torch.mean(torch.stack(self.get_tower_stats("mean_kl_loss"))),
"entropy":
torch.mean(torch.stack(self.get_tower_stats("mean_entropy"))),
"entropy_coeff":
self.entropy_coeff,
})
def _process_policy_eval_results(
*,
to_eval: Dict[PolicyID, List[PolicyEvalData]],
eval_results: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]],
active_episodes: Dict[str, MultiAgentEpisode],
active_envs: Set[int],
off_policy_actions: MultiEnvDict,
policies: Dict[PolicyID, Policy],
clip_actions: bool,
) -> Dict[EnvID, Dict[AgentID, EnvActionType]]:
"""Process the output of policy neural network evaluation.
Records policy evaluation results into the given episode objects and
returns replies to send back to agents in the env.
Args:
to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy IDs
to lists of PolicyEvalData objects.
eval_results (Dict[PolicyID, List]): Mapping of policy IDs to list of
actions, rnn-out states, extra-action-fetches dicts.
active_episodes (Dict[str, MultiAgentEpisode]): Mapping from
episode ID to currently ongoing MultiAgentEpisode object.
active_envs (Set[int]): Set of non-terminated env ids.
off_policy_actions (dict): Doubly keyed dict of env-ids -> agent ids ->
off-policy-action, returned by a `BaseEnv.poll()` call.
policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy.
clip_actions (bool): Whether to clip actions to the action space's
bounds.
Returns:
actions_to_send: Nested dict of env id -> agent id -> actions to be
sent to Env (np.ndarrays).
"""
actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \
defaultdict(dict)
# type: int
for env_id in active_envs:
actions_to_send[env_id] = {} # at minimum send empty dict
# type: PolicyID, List[PolicyEvalData]
for policy_id, eval_data in to_eval.items():
actions: TensorStructType = eval_results[policy_id][0]
actions = convert_to_numpy(actions)
rnn_out_cols: StateBatch = eval_results[policy_id][1]
pi_info_cols: dict = eval_results[policy_id][2]
# In case actions is a list (representing the 0th dim of a batch of
# primitive actions), try to convert it first.
if isinstance(actions, list):
actions = np.array(actions)
# Store RNN state ins/outs and extra-action fetches to episode.
for f_i, column in enumerate(rnn_out_cols):
pi_info_cols["state_out_{}".format(f_i)] = column
policy: Policy = _get_or_raise(policies, policy_id)
# Split action-component batches into single action rows.
actions: List[EnvActionType] = unbatch(actions)
# type: int, EnvActionType
for i, action in enumerate(actions):
# Clip if necessary.
if clip_actions:
clipped_action = clip_action(action,
policy.action_space_struct)
else:
clipped_action = action
env_id: int = eval_data[i].env_id
agent_id: AgentID = eval_data[i].agent_id
episode: MultiAgentEpisode = active_episodes[env_id]
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
episode._set_last_pi_info(
agent_id, {k: v[i]
for k, v in pi_info_cols.items()})
if env_id in off_policy_actions and \
agent_id in off_policy_actions[env_id]:
episode._set_last_action(agent_id,
off_policy_actions[env_id][agent_id])
else:
episode._set_last_action(agent_id, action)
assert agent_id not in actions_to_send[env_id]
actions_to_send[env_id][agent_id] = clipped_action
return actions_to_send
def _compute_action_helper(self, input_dict, state_batches, seq_lens,
explore, timestep):
"""Shared forward pass logic (w/ and w/o trajectory view API).
Returns:
A tuple consisting of a) actions, b) state_out, c) extra_fetches.
"""
explore = explore if explore is not None else self.config["explore"]
timestep = timestep if timestep is not None else self.global_timestep
self._is_recurrent = state_batches is not None and state_batches != []
# Switch to eval mode.
if self.model:
self.model.eval()
if is_overridden(self.action_sampler_fn):
action_dist = dist_inputs = None
actions, logp, state_out = self.action_sampler_fn(
self.model,
obs_batch=input_dict,
state_batches=state_batches,
explore=explore,
timestep=timestep,
)
else:
# Call the exploration before_compute_actions hook.
self.exploration.before_compute_actions(explore=explore,
timestep=timestep)
if is_overridden(self.action_distribution_fn):
dist_inputs, dist_class, state_out = self.action_distribution_fn(
self.model,
obs_batch=input_dict,
state_batches=state_batches,
seq_lens=seq_lens,
explore=explore,
timestep=timestep,
is_training=False,
)
else:
dist_class = self.dist_class
dist_inputs, state_out = self.model(input_dict, state_batches,
seq_lens)
if not (isinstance(dist_class, functools.partial)
or issubclass(dist_class, TorchDistributionWrapper)):
raise ValueError(
"`dist_class` ({}) not a TorchDistributionWrapper "
"subclass! Make sure your `action_distribution_fn` or "
"`make_model_and_action_dist` return a correct "
"distribution class.".format(dist_class.__name__))
action_dist = dist_class(dist_inputs, self.model)
# Get the exploration action from the forward results.
actions, logp = self.exploration.get_exploration_action(
action_distribution=action_dist,
timestep=timestep,
explore=explore)
input_dict[SampleBatch.ACTIONS] = actions
# Add default and custom fetches.
extra_fetches = self.extra_action_out(input_dict, state_batches,
self.model, action_dist)
# Action-dist inputs.
if dist_inputs is not None:
extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = dist_inputs
# Action-logp and action-prob.
if logp is not None:
extra_fetches[SampleBatch.ACTION_PROB] = torch.exp(logp.float())
extra_fetches[SampleBatch.ACTION_LOGP] = logp
# Update our global timestep by the batch size.
self.global_timestep += len(input_dict[SampleBatch.CUR_OBS])
return convert_to_numpy((actions, state_out, extra_fetches))
