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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import defaultdict
import random
import numpy as np
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.utils.annotations import DeveloperAPI
class MultiAgentEpisode(object):
"""Tracks the current state of a (possibly multi-agent) episode.
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.
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, policy_mapping_fn, batch_builder_factory,
self.new_batch_builder = batch_builder_factory
self.add_extra_batch = extra_batch_callback
self.batch_builder = batch_builder_factory()
self.total_reward = 0.0
self.length = 0
self.episode_id = random.randrange(2e9)
self.agent_rewards = defaultdict(float)
self.custom_metrics = {}
self.user_data = {}
self._policies = policies
self._policy_mapping_fn = policy_mapping_fn
self._next_agent_index = 0
self._agent_to_index = {}
self._agent_to_policy = {}
self._agent_to_rnn_state = {}
self._agent_to_last_obs = {}
self._agent_to_last_raw_obs = {}
self._agent_to_last_info = {}
self._agent_to_last_action = {}
self._agent_to_last_pi_info = {}
self._agent_to_prev_action = {}
self._agent_reward_history = defaultdict(list)
def soft_reset(self):
"""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)
def policy_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the policy 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.
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]
def last_observation_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the last observation for the specified agent."""
return self._agent_to_last_obs.get(agent_id)
def last_raw_obs_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the last un-preprocessed obs for the specified agent."""
return self._agent_to_last_raw_obs.get(agent_id)
def last_info_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the last info for the specified agent."""
return self._agent_to_last_info.get(agent_id)
def last_action_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the last action for the specified agent, or zeros."""
if agent_id in self._agent_to_last_action:
return _flatten_action(self._agent_to_last_action[agent_id])
policy = self._policies[self.policy_for(agent_id)]
flat = _flatten_action(policy.action_space.sample())
return np.zeros_like(flat)
def prev_action_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the previous action for the specified agent."""
if agent_id in self._agent_to_prev_action:
return _flatten_action(self._agent_to_prev_action[agent_id])
# We're at t=0, so return all zeros.
return np.zeros_like(self.last_action_for(agent_id))
def prev_reward_for(self, agent_id=_DUMMY_AGENT_ID):
"""Returns the previous reward for the specified agent."""
history = self._agent_reward_history[agent_id]
if len(history) >= 2:
return history[-2]
# We're at t=0, so there is no previous reward, just return zero.
return 0.0
def rnn_state_for(self, agent_id=_DUMMY_AGENT_ID):
"""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]
def last_pi_info_for(self, agent_id=_DUMMY_AGENT_ID):
"""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):
for agent_id, reward in reward_dict.items():
if reward is not None:
self.policy_for(agent_id)] += reward
self.total_reward += 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] = 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]
def _flatten_action(action):
# Concatenate tuple actions
if isinstance(action, list) or isinstance(action, tuple):
expanded = []
for a in action:
expanded.append(np.reshape(a, [-1]))
action = np.concatenate(expanded, axis=0).flatten()
return action
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