mirror of
https://github.com/vale981/ray
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286 lines
12 KiB
Python
286 lines
12 KiB
Python
from gym.spaces import Box, MultiDiscrete, Tuple as TupleSpace
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import logging
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import numpy as np
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import random
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import time
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from typing import Callable, Optional, Tuple
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.typing import MultiAgentDict, PolicyID, AgentID
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logger = logging.getLogger(__name__)
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class Unity3DEnv(MultiAgentEnv):
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"""A MultiAgentEnv representing a single Unity3D game instance.
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For an example on how to use this Env with a running Unity3D editor
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or with a compiled game, see:
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`rllib/examples/unity3d_env_local.py`
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For an example on how to use it inside a Unity game client, which
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connects to an RLlib Policy server, see:
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`rllib/examples/serving/unity3d_[client|server].py`
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Supports all Unity3D (MLAgents) examples, multi- or single-agent and
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gets converted automatically into an ExternalMultiAgentEnv, when used
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inside an RLlib PolicyClient for cloud/distributed training of Unity games.
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"""
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# Default base port when connecting directly to the Editor
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_BASE_PORT_EDITOR = 5004
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# Default base port when connecting to a compiled environment
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_BASE_PORT_ENVIRONMENT = 5005
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# The worker_id for each environment instance
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_WORKER_ID = 0
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def __init__(self,
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file_name: str = None,
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port: Optional[int] = None,
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seed: int = 0,
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no_graphics: bool = False,
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timeout_wait: int = 300,
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episode_horizon: int = 1000):
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"""Initializes a Unity3DEnv object.
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Args:
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file_name (Optional[str]): Name of the Unity game binary.
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If None, will assume a locally running Unity3D editor
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to be used, instead.
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port (Optional[int]): Port number to connect to Unity environment.
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seed (int): A random seed value to use for the Unity3D game.
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no_graphics (bool): Whether to run the Unity3D simulator in
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no-graphics mode. Default: False.
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timeout_wait (int): Time (in seconds) to wait for connection from
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the Unity3D instance.
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episode_horizon (int): A hard horizon to abide to. After at most
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this many steps (per-agent episode `step()` calls), the
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Unity3D game is reset and will start again (finishing the
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multi-agent episode that the game represents).
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Note: The game itself may contain its own episode length
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limits, which are always obeyed (on top of this value here).
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"""
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super().__init__()
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if file_name is None:
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print(
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"No game binary provided, will use a running Unity editor "
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"instead.\nMake sure you are pressing the Play (|>) button in "
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"your editor to start.")
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import mlagents_envs
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from mlagents_envs.environment import UnityEnvironment
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# Try connecting to the Unity3D game instance. If a port is blocked
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while True:
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# Sleep for random time to allow for concurrent startup of many
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# environments (num_workers >> 1). Otherwise, would lead to port
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# conflicts sometimes.
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time.sleep(random.randint(1, 10))
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port_ = port or (self._BASE_PORT_ENVIRONMENT
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if file_name else self._BASE_PORT_EDITOR)
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# cache the worker_id and
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# increase it for the next environment
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worker_id_ = Unity3DEnv._WORKER_ID if file_name else 0
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Unity3DEnv._WORKER_ID += 1
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try:
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self.unity_env = UnityEnvironment(
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file_name=file_name,
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worker_id=worker_id_,
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base_port=port_,
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seed=seed,
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no_graphics=no_graphics,
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timeout_wait=timeout_wait,
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)
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print(
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"Created UnityEnvironment for port {}".format(port_ +
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worker_id_))
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except mlagents_envs.exception.UnityWorkerInUseException:
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pass
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else:
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break
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# Reset entire env every this number of step calls.
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self.episode_horizon = episode_horizon
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# Keep track of how many times we have called `step` so far.
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self.episode_timesteps = 0
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@override(MultiAgentEnv)
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def step(
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self, action_dict: MultiAgentDict
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) -> Tuple[MultiAgentDict, MultiAgentDict, MultiAgentDict, MultiAgentDict]:
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"""Performs one multi-agent step through the game.
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Args:
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action_dict (dict): Multi-agent action dict with:
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keys=agent identifier consisting of
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[MLagents behavior name, e.g. "Goalie?team=1"] + "_" +
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[Agent index, a unique MLAgent-assigned index per single agent]
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Returns:
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tuple:
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- obs: Multi-agent observation dict.
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Only those observations for which to get new actions are
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returned.
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- rewards: Rewards dict matching `obs`.
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- dones: Done dict with only an __all__ multi-agent entry in
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it. __all__=True, if episode is done for all agents.
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- infos: An (empty) info dict.
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"""
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# Set only the required actions (from the DecisionSteps) in Unity3D.
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all_agents = []
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for behavior_name in self.unity_env.behavior_specs:
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for agent_id in self.unity_env.get_steps(behavior_name)[
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0].agent_id_to_index.keys():
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key = behavior_name + "_{}".format(agent_id)
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all_agents.append(key)
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self.unity_env.set_action_for_agent(behavior_name, agent_id,
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action_dict[key])
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# Do the step.
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self.unity_env.step()
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obs, rewards, dones, infos = self._get_step_results()
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# Global horizon reached? -> Return __all__ done=True, so user
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# can reset. Set all agents' individual `done` to True as well.
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self.episode_timesteps += 1
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if self.episode_timesteps > self.episode_horizon:
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return obs, rewards, dict({
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"__all__": True
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}, **{agent_id: True
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for agent_id in all_agents}), infos
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return obs, rewards, dones, infos
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@override(MultiAgentEnv)
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def reset(self) -> MultiAgentDict:
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"""Resets the entire Unity3D scene (a single multi-agent episode)."""
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self.episode_timesteps = 0
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self.unity_env.reset()
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obs, _, _, _ = self._get_step_results()
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return obs
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def _get_step_results(self):
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"""Collects those agents' obs/rewards that have to act in next `step`.
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Returns:
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Tuple:
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obs: Multi-agent observation dict.
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Only those observations for which to get new actions are
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returned.
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rewards: Rewards dict matching `obs`.
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dones: Done dict with only an __all__ multi-agent entry in it.
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__all__=True, if episode is done for all agents.
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infos: An (empty) info dict.
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"""
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obs = {}
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rewards = {}
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infos = {}
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for behavior_name in self.unity_env.behavior_specs:
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decision_steps, terminal_steps = self.unity_env.get_steps(
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behavior_name)
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# Important: Only update those sub-envs that are currently
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# available within _env_state.
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# Loop through all envs ("agents") and fill in, whatever
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# information we have.
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for agent_id, idx in decision_steps.agent_id_to_index.items():
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key = behavior_name + "_{}".format(agent_id)
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os = tuple(o[idx] for o in decision_steps.obs)
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os = os[0] if len(os) == 1 else os
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obs[key] = os
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rewards[key] = decision_steps.reward[idx] # rewards vector
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for agent_id, idx in terminal_steps.agent_id_to_index.items():
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key = behavior_name + "_{}".format(agent_id)
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# Only overwrite rewards (last reward in episode), b/c obs
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# here is the last obs (which doesn't matter anyways).
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# Unless key does not exist in obs.
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if key not in obs:
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os = tuple(o[idx] for o in terminal_steps.obs)
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obs[key] = os = os[0] if len(os) == 1 else os
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rewards[key] = terminal_steps.reward[idx] # rewards vector
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# Only use dones if all agents are done, then we should do a reset.
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return obs, rewards, {"__all__": False}, infos
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@staticmethod
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def get_policy_configs_for_game(
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game_name: str) -> Tuple[dict, Callable[[AgentID], PolicyID]]:
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# The RLlib server must know about the Spaces that the Client will be
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# using inside Unity3D, up-front.
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obs_spaces = {
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# 3DBall.
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"3DBall": Box(float("-inf"), float("inf"), (8, )),
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# 3DBallHard.
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"3DBallHard": Box(float("-inf"), float("inf"), (45, )),
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# Pyramids.
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"Pyramids": TupleSpace([
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Box(float("-inf"), float("inf"), (56, )),
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Box(float("-inf"), float("inf"), (56, )),
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Box(float("-inf"), float("inf"), (56, )),
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Box(float("-inf"), float("inf"), (4, )),
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]),
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# SoccerStrikersVsGoalie.
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"Goalie": Box(float("-inf"), float("inf"), (738, )),
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"Striker": TupleSpace([
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Box(float("-inf"), float("inf"), (231, )),
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Box(float("-inf"), float("inf"), (63, )),
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]),
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# Tennis.
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"Tennis": Box(float("-inf"), float("inf"), (27, )),
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# VisualHallway.
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"VisualHallway": Box(float("-inf"), float("inf"), (84, 84, 3)),
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# Walker.
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"Walker": Box(float("-inf"), float("inf"), (212, )),
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# FoodCollector.
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"FoodCollector": TupleSpace([
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Box(float("-inf"), float("inf"), (49, )),
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Box(float("-inf"), float("inf"), (4, )),
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]),
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}
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action_spaces = {
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# 3DBall.
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"3DBall": Box(
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float("-inf"), float("inf"), (2, ), dtype=np.float32),
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# 3DBallHard.
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"3DBallHard": Box(
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float("-inf"), float("inf"), (2, ), dtype=np.float32),
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# Pyramids.
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"Pyramids": MultiDiscrete([5]),
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# SoccerStrikersVsGoalie.
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"Goalie": MultiDiscrete([3, 3, 3]),
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"Striker": MultiDiscrete([3, 3, 3]),
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# Tennis.
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"Tennis": Box(float("-inf"), float("inf"), (3, )),
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# VisualHallway.
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"VisualHallway": MultiDiscrete([5]),
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# Walker.
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"Walker": Box(float("-inf"), float("inf"), (39, )),
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# FoodCollector.
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"FoodCollector": MultiDiscrete([3, 3, 3, 2]),
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}
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# Policies (Unity: "behaviors") and agent-to-policy mapping fns.
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if game_name == "SoccerStrikersVsGoalie":
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policies = {
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"Goalie": (None, obs_spaces["Goalie"], action_spaces["Goalie"],
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{}),
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"Striker": (None, obs_spaces["Striker"],
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action_spaces["Striker"], {}),
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}
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def policy_mapping_fn(agent_id):
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return "Striker" if "Striker" in agent_id else "Goalie"
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else:
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policies = {
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game_name: (None, obs_spaces[game_name],
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action_spaces[game_name], {}),
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}
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def policy_mapping_fn(agent_id):
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return game_name
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return policies, policy_mapping_fn
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