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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
from ray.rllib.utils.annotations import PublicAPI
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@PublicAPI
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class MultiAgentEnv:
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"""An environment that hosts multiple independent agents.
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Agents are identified by (string) agent ids. Note that these "agents" here
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are not to be confused with RLlib agents.
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Examples:
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>>> env = MyMultiAgentEnv()
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>>> obs = env.reset()
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>>> print(obs)
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{
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"car_0": [2.4, 1.6],
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"car_1": [3.4, -3.2],
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"traffic_light_1": [0, 3, 5, 1],
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}
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>>> obs, rewards, dones, infos = env.step(
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action_dict={
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"car_0": 1, "car_1": 0, "traffic_light_1": 2,
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})
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>>> print(rewards)
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{
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"car_0": 3,
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"car_1": -1,
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"traffic_light_1": 0,
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}
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>>> print(dones)
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{
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"car_0": False, # car_0 is still running
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"car_1": True, # car_1 is done
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"__all__": False, # the env is not done
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}
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>>> print(infos)
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{
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"car_0": {}, # info for car_0
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"car_1": {}, # info for car_1
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}
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"""
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@PublicAPI
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def reset(self):
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"""Resets the env and returns observations from ready agents.
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Returns:
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obs (dict): New observations for each ready agent.
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"""
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raise NotImplementedError
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@PublicAPI
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def step(self, action_dict):
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"""Returns observations from ready agents.
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The returns are dicts mapping from agent_id strings to values. The
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number of agents in the env can vary over time.
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Returns
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-------
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obs (dict): New observations for each ready agent.
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rewards (dict): Reward values for each ready agent. If the
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episode is just started, the value will be None.
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dones (dict): Done values for each ready agent. The special key
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"__all__" (required) is used to indicate env termination.
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infos (dict): Optional info values for each agent id.
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"""
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raise NotImplementedError
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# yapf: disable
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# __grouping_doc_begin__
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@PublicAPI
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def with_agent_groups(self, groups, obs_space=None, act_space=None):
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"""Convenience method for grouping together agents in this env.
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An agent group is a list of agent ids that are mapped to a single
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logical agent. All agents of the group must act at the same time in the
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environment. The grouped agent exposes Tuple action and observation
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spaces that are the concatenated action and obs spaces of the
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individual agents.
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The rewards of all the agents in a group are summed. The individual
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agent rewards are available under the "individual_rewards" key of the
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group info return.
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Agent grouping is required to leverage algorithms such as Q-Mix.
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This API is experimental.
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Arguments:
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groups (dict): Mapping from group id to a list of the agent ids
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of group members. If an agent id is not present in any group
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value, it will be left ungrouped.
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obs_space (Space): Optional observation space for the grouped
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env. Must be a tuple space.
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act_space (Space): Optional action space for the grouped env.
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Must be a tuple space.
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Examples:
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>>> env = YourMultiAgentEnv(...)
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>>> grouped_env = env.with_agent_groups(env, {
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... "group1": ["agent1", "agent2", "agent3"],
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... "group2": ["agent4", "agent5"],
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... })
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"""
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from ray.rllib.env.group_agents_wrapper import _GroupAgentsWrapper
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return _GroupAgentsWrapper(self, groups, obs_space, act_space)
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# __grouping_doc_end__
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# yapf: enable
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