Source code for ray.rllib.evaluation.episode

from collections import defaultdict
import numpy as np
import random
from typing import List, Dict, Callable, Any, TYPE_CHECKING

from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import DeveloperAPI
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.rllib.evaluation.sample_batch_builder import \

[docs]@DeveloperAPI class MultiAgentEpisode: """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: Dict[PolicyID, Policy], policy_mapping_fn: Callable[[AgentID], PolicyID], batch_builder_factory: Callable[ [], "MultiAgentSampleBatchBuilder"], extra_batch_callback: Callable[[SampleBatchType], None], env_id: EnvID): 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.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]] = {} Dict[str, Any] = {} self._policies: Dict[PolicyID, Policy] = policies self._policy_mapping_fn: Callable[[AgentID], 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_info: Dict[AgentID, EnvInfoDict] = {} self._agent_to_last_action: Dict[AgentID, EnvActionType] = {} self._agent_to_last_pi_info: Dict[AgentID, dict] = {} self._agent_to_prev_action: Dict[AgentID, EnvActionType] = {} self._agent_reward_history: Dict[AgentID, List[int]] = defaultdict( list)
[docs] @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)
[docs] @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 episode. Args: agent_id (AgentID): The agent ID to lookup the policy ID for. Returns: PolicyID: The policy ID for the specified agent. """ if agent_id not in self._agent_to_policy: self._agent_to_policy[agent_id] = self._policy_mapping_fn(agent_id) return self._agent_to_policy[agent_id]
[docs] @DeveloperAPI def last_observation_for( self, agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvObsType: """Returns the last observation for the specified agent.""" return self._agent_to_last_obs.get(agent_id)
[docs] @DeveloperAPI def last_raw_obs_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvObsType: """Returns the last un-preprocessed obs for the specified agent.""" return self._agent_to_last_raw_obs.get(agent_id)
[docs] @DeveloperAPI def last_info_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvInfoDict: """Returns the last info for the specified agent.""" return self._agent_to_last_info.get(agent_id)
[docs] @DeveloperAPI def last_action_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvActionType: """Returns the last action for the specified agent, or zeros.""" if agent_id in self._agent_to_last_action: return flatten_to_single_ndarray( self._agent_to_last_action[agent_id]) else: policy = self._policies[self.policy_for(agent_id)] 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)
[docs] @DeveloperAPI def prev_action_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> EnvActionType: """Returns the previous action for the specified agent.""" if agent_id in self._agent_to_prev_action: return flatten_to_single_ndarray( self._agent_to_prev_action[agent_id]) else: # We're at t=0, so return all zeros. return np.zeros_like(self.last_action_for(agent_id))
[docs] @DeveloperAPI def prev_reward_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> float: """Returns the previous reward for the specified agent.""" history = self._agent_reward_history[agent_id] if len(history) >= 2: return history[-2] else: # We're at t=0, so there is no previous reward, just return zero. return 0.0
[docs] @DeveloperAPI def rnn_state_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> List[Any]: """Returns the last RNN state for the specified agent.""" if agent_id not in self._agent_to_rnn_state: policy = self._policies[self.policy_for(agent_id)] self._agent_to_rnn_state[agent_id] = policy.get_initial_state() return self._agent_to_rnn_state[agent_id]
[docs] @DeveloperAPI def last_pi_info_for(self, agent_id: AgentID = _DUMMY_AGENT_ID) -> dict: """Returns the last info object for the specified agent.""" return self._agent_to_last_pi_info[agent_id]
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_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_pi_info(self, agent_id, pi_info): self._agent_to_last_pi_info[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]