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from collections import defaultdict
import numpy as np
import random
import tree # pip install dm_tree
from typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.policy.policy_map import PolicyMap
from ray.rllib.utils.annotations import Deprecated, DeveloperAPI
from ray.rllib.utils.deprecation import deprecation_warning
from ray.rllib.utils.spaces.space_utils import flatten_to_single_ndarray
from ray.rllib.utils.typing import SampleBatchType, AgentID, PolicyID, \
EnvActionType, EnvID, EnvInfoDict, EnvObsType
from ray.util import log_once
if TYPE_CHECKING:
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.evaluation.sample_batch_builder import \
MultiAgentSampleBatchBuilder
@DeveloperAPI
class Episode:
"""Tracks the current state of a (possibly multi-agent) episode.
Attributes:
new_batch_builder (func): Create a new MultiAgentSampleBatchBuilder.
add_extra_batch (func): Return a built MultiAgentBatch to the sampler.
batch_builder (obj): Batch builder for the current episode.
total_reward (float): Summed reward across all agents in this episode.
length (int): Length of this episode.
episode_id (int): Unique id identifying this trajectory.
agent_rewards (dict): Summed rewards broken down by agent.
custom_metrics (dict): Dict where the you can add custom metrics.
user_data (dict): Dict that you can use for temporary storage. E.g.
in between two custom callbacks referring to the same episode.
hist_data (dict): Dict mapping str keys to List[float] for storage of
per-timestep float data throughout the episode.
Use case 1: Model-based rollouts in multi-agent:
A custom compute_actions() function in a policy can inspect the
current episode state and perform a number of rollouts based on the
policies and state of other agents in the environment.
Use case 2: Returning extra rollouts data.
The model rollouts can be returned back to the sampler by calling:
>>> batch = episode.new_batch_builder()
>>> for each transition:
batch.add_values(...) # see sampler for usage
>>> episode.extra_batches.add(batch.build_and_reset())
"""
def __init__(
self,
policies: PolicyMap,
policy_mapping_fn: Callable[[AgentID, "Episode", "RolloutWorker"],
PolicyID],
batch_builder_factory: Callable[[],
"MultiAgentSampleBatchBuilder"],
extra_batch_callback: Callable[[SampleBatchType], None],
env_id: EnvID,
*,
worker: Optional["RolloutWorker"] = None,
):
"""Initializes an Episode instance.
Args:
policies: The PolicyMap object (mapping PolicyIDs to Policy
objects) to use for determining, which policy is used for
which agent.
policy_mapping_fn: The mapping function mapping AgentIDs to
PolicyIDs.
batch_builder_factory:
extra_batch_callback:
env_id: The environment's ID in which this episode runs.
worker: The RolloutWorker instance, in which this episode runs.
"""
self.new_batch_builder: Callable[
[], "MultiAgentSampleBatchBuilder"] = batch_builder_factory
self.add_extra_batch: Callable[[SampleBatchType],
None] = extra_batch_callback
self.batch_builder: "MultiAgentSampleBatchBuilder" = \
batch_builder_factory()
self.total_reward: float = 0.0
self.length: int = 0
self.episode_id: int = random.randrange(2e9)
self.env_id = env_id
self.worker = worker
self.agent_rewards: Dict[AgentID, float] = defaultdict(float)
self.custom_metrics: Dict[str, float] = {}
self.user_data: Dict[str, Any] = {}
self.hist_data: Dict[str, List[float]] = {}
self.media: Dict[str, Any] = {}
self.policy_map: PolicyMap = policies
self._policies = self.policy_map # backward compatibility
self.policy_mapping_fn: Callable[[AgentID, "Episode", "RolloutWorker"],
PolicyID] = policy_mapping_fn
self._next_agent_index: int = 0
self._agent_to_index: Dict[AgentID, int] = {}
self._agent_to_policy: Dict[AgentID, PolicyID] = {}
self._agent_to_rnn_state: Dict[AgentID, List[Any]] = {}
self._agent_to_last_obs: Dict[AgentID, EnvObsType] = {}
self._agent_to_last_raw_obs: Dict[AgentID, EnvObsType] = {}
self._agent_to_last_done: Dict[AgentID, bool] = {}
self._agent_to_last_info: Dict[AgentID, EnvInfoDict] = {}
self._agent_to_last_action: Dict[AgentID, EnvActionType] = {}
self._agent_to_last_extra_action_outs: Dict[AgentID, dict] = {}
self._agent_to_prev_action: Dict[AgentID, EnvActionType] = {}
self._agent_reward_history: Dict[AgentID, List[int]] = defaultdict(
list)
@DeveloperAPI
def soft_reset(self) -> None:
"""Clears rewards and metrics, but retains RNN and other state.
This is used to carry state across multiple logical episodes in the
same env (i.e., if `soft_horizon` is set).
"""
self.length = 0
self.episode_id = random.randrange(2e9)
self.total_reward = 0.0
self.agent_rewards = defaultdict(float)
self._agent_reward_history = defaultdict(list)
@DeveloperAPI
def policy_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> PolicyID:
"""Returns and stores the policy ID for the specified agent.
If the agent is new, the policy mapping fn will be called to bind the
agent to a policy for the duration of the entire episode (even if the
policy_mapping_fn is changed in the meantime!).
Args:
agent_id: The agent ID to lookup the policy ID for.
Returns:
The policy ID for the specified agent.
"""
# Perform a new policy_mapping_fn lookup and bind AgentID for the
# duration of this episode to the returned PolicyID.
if agent_id not in self._agent_to_policy:
# Try new API: pass in agent_id and episode as named args.
# New signature should be: (agent_id, episode, worker, **kwargs)
try:
policy_id = self._agent_to_policy[agent_id] = \
self.policy_mapping_fn(agent_id, self, worker=self.worker)
except TypeError as e:
if "positional argument" in e.args[0] or \
"unexpected keyword argument" in e.args[0]:
if log_once("policy_mapping_new_signature"):
deprecation_warning(
old="policy_mapping_fn(agent_id)",
new="policy_mapping_fn(agent_id, episode, "
"worker, **kwargs)")
policy_id = self._agent_to_policy[agent_id] = \
self.policy_mapping_fn(agent_id)
else:
raise e
# Use already determined PolicyID.
else:
policy_id = self._agent_to_policy[agent_id]
# PolicyID not found in policy map -> Error.
if policy_id not in self.policy_map:
raise KeyError("policy_mapping_fn returned invalid policy id "
f"'{policy_id}'!")
return policy_id
@DeveloperAPI
def last_observation_for(
self, agent_id: AgentID = _DUMMY_AGENT_ID) -> Optional[EnvObsType]:
"""Returns the last observation for the specified AgentID.
Args:
agent_id: The agent's ID to get the last observation for.
Returns:
Last observation the specified AgentID has seen. None in case
the agent has never made any observations in the episode.
"""
return self._agent_to_last_obs.get(agent_id)
@DeveloperAPI
def last_raw_obs_for(
self, agent_id: AgentID = _DUMMY_AGENT_ID) -> Optional[EnvObsType]:
"""Returns the last un-preprocessed obs for the specified AgentID.
Args:
agent_id: The agent's ID to get the last un-preprocessed
observation for.
Returns:
Last un-preprocessed observation the specified AgentID has seen.
None in case the agent has never made any observations in the
episode.
"""
return self._agent_to_last_raw_obs.get(agent_id)
@DeveloperAPI
def last_info_for(self, agent_id: AgentID = _DUMMY_AGENT_ID
) -> Optional[EnvInfoDict]:
"""Returns the last info for the specified AgentID.
Args:
agent_id: The agent's ID to get the last info for.
Returns:
Last info dict the specified AgentID has seen.
None in case the agent has never made any observations in the
episode.
"""
return self._agent_to_last_info.get(agent_id)
@DeveloperAPI
def last_action_for(self,
agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvActionType:
"""Returns the last action for the specified AgentID, or zeros.
The "last" action is the most recent one taken by the agent.
Args:
agent_id: The agent's ID to get the last action for.
Returns:
Last action the specified AgentID has executed.
Zeros in case the agent has never performed any actions in the
episode.
"""
policy_id = self.policy_for(agent_id)
policy = self.policy_map[policy_id]
# Agent has already taken at least one action in the episode.
if agent_id in self._agent_to_last_action:
if policy.config.get("_disable_action_flattening"):
return self._agent_to_last_action[agent_id]
else:
return flatten_to_single_ndarray(
self._agent_to_last_action[agent_id])
# Agent has not acted yet, return all zeros.
else:
if policy.config.get("_disable_action_flattening"):
return tree.map_structure(
lambda s: np.zeros_like(s.sample(), s.dtype) if
hasattr(s, "dtype") else np.zeros_like(s.sample()),
policy.action_space_struct,
)
else:
flat = flatten_to_single_ndarray(policy.action_space.sample())
if hasattr(policy.action_space, "dtype"):
return np.zeros_like(flat, dtype=policy.action_space.dtype)
return np.zeros_like(flat)
@DeveloperAPI
def prev_action_for(self,
agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvActionType:
"""Returns the previous action for the specified agent, or zeros.
The "previous" action is the one taken one timestep before the
most recent action taken by the agent.
Args:
agent_id: The agent's ID to get the previous action for.
Returns:
Previous action the specified AgentID has executed.
Zero in case the agent has never performed any actions (or only
one) in the episode.
"""
policy_id = self.policy_for(agent_id)
policy = self.policy_map[policy_id]
# We are at t > 1 -> There has been a previous action by this agent.
if agent_id in self._agent_to_prev_action:
if policy.config.get("_disable_action_flattening"):
return self._agent_to_prev_action[agent_id]
else:
return flatten_to_single_ndarray(
self._agent_to_prev_action[agent_id])
# We're at t <= 1, so return all zeros.
else:
if policy.config.get("_disable_action_flattening"):
return tree.map_structure(
lambda a: np.zeros_like(a, a.dtype) if # noqa
hasattr(a, "dtype") else np.zeros_like(a), # noqa
self.last_action_for(agent_id),
)
else:
return np.zeros_like(self.last_action_for(agent_id))
@DeveloperAPI
def last_reward_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> float:
"""Returns the last reward for the specified agent, or zero.
The "last" reward is the one received most recently by the agent.
Args:
agent_id: The agent's ID to get the last reward for.
Returns:
Last reward for the the specified AgentID.
Zero in case the agent has never performed any actions
(and thus received rewards) in the episode.
"""
history = self._agent_reward_history[agent_id]
# We are at t > 0 -> Return previously received reward.
if len(history) >= 1:
return history[-1]
# We're at t=0, so there is no previous reward, just return zero.
else:
return 0.0
@DeveloperAPI
def prev_reward_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> float:
"""Returns the previous reward for the specified agent, or zero.
The "previous" reward is the one received one timestep before the
most recently received reward of the agent.
Args:
agent_id: The agent's ID to get the previous reward for.
Returns:
Previous reward for the the specified AgentID.
Zero in case the agent has never performed any actions (or only
one) in the episode.
"""
history = self._agent_reward_history[agent_id]
# We are at t > 1 -> Return reward prior to most recent (last) one.
if len(history) >= 2:
return history[-2]
# We're at t <= 1, so there is no previous reward, just return zero.
else:
return 0.0
@DeveloperAPI
def rnn_state_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> List[Any]:
"""Returns the last RNN state for the specified agent.
Args:
agent_id: The agent's ID to get the most recent RNN state for.
Returns:
Most recent RNN state of the the specified AgentID.
"""
if agent_id not in self._agent_to_rnn_state:
policy_id = self.policy_for(agent_id)
policy = self.policy_map[policy_id]
self._agent_to_rnn_state[agent_id] = policy.get_initial_state()
return self._agent_to_rnn_state[agent_id]
@DeveloperAPI
def last_done_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> bool:
"""Returns the last done flag for the specified AgentID.
Args:
agent_id: The agent's ID to get the last done flag for.
Returns:
Last done flag for the specified AgentID.
"""
if agent_id not in self._agent_to_last_done:
self._agent_to_last_done[agent_id] = False
return self._agent_to_last_done[agent_id]
@DeveloperAPI
def last_extra_action_outs_for(
self,
agent_id: AgentID = _DUMMY_AGENT_ID,
) -> dict:
"""Returns the last extra-action outputs for the specified agent.
This data is returned by a call to
`Policy.compute_actions_from_input_dict` as the 3rd return value
(1st return value = action; 2nd return value = RNN state outs).
Args:
agent_id: The agent's ID to get the last extra-action outs for.
Returns:
The last extra-action outs for the specified AgentID.
"""
return self._agent_to_last_extra_action_outs[agent_id]
@DeveloperAPI
def get_agents(self) -> List[AgentID]:
"""Returns list of agent IDs that have appeared in this episode.
Returns:
The list of all agent IDs that have appeared so far in this
episode.
"""
return list(self._agent_to_index.keys())
def _add_agent_rewards(self, reward_dict: Dict[AgentID, float]) -> None:
for agent_id, reward in reward_dict.items():
if reward is not None:
self.agent_rewards[agent_id,
self.policy_for(agent_id)] += reward
self.total_reward += reward
self._agent_reward_history[agent_id].append(reward)
def _set_rnn_state(self, agent_id, rnn_state):
self._agent_to_rnn_state[agent_id] = rnn_state
def _set_last_observation(self, agent_id, obs):
self._agent_to_last_obs[agent_id] = obs
def _set_last_raw_obs(self, agent_id, obs):
self._agent_to_last_raw_obs[agent_id] = obs
def _set_last_done(self, agent_id, done):
self._agent_to_last_done[agent_id] = done
def _set_last_info(self, agent_id, info):
self._agent_to_last_info[agent_id] = info
def _set_last_action(self, agent_id, action):
if agent_id in self._agent_to_last_action:
self._agent_to_prev_action[agent_id] = \
self._agent_to_last_action[agent_id]
self._agent_to_last_action[agent_id] = action
def _set_last_extra_action_outs(self, agent_id, pi_info):
self._agent_to_last_extra_action_outs[agent_id] = pi_info
def _agent_index(self, agent_id):
if agent_id not in self._agent_to_index:
self._agent_to_index[agent_id] = self._next_agent_index
self._next_agent_index += 1
return self._agent_to_index[agent_id]
@property
def _policy_mapping_fn(self):
deprecation_warning(
old="Episode._policy_mapping_fn",
new="Episode.policy_mapping_fn",
error=False,
)
return self.policy_mapping_fn
@Deprecated(new="Episode.last_extra_action_outs_for", error=False)
def last_pi_info_for(self, *args, **kwargs):
return self.last_extra_action_outs_for(*args, **kwargs)
# Backward compatibility. The name Episode implies that there is
# also a (single agent?) Episode.
@Deprecated(new="ray.rllib.evaluation.episode.Episode", error=False)
class MultiAgentEpisode(Episode):
pass