ml-agents/gym-unity/gym_unity/envs/unity_env.py

import logging
import itertools
import gym
import numpy as np
from mlagents.envs import UnityEnvironment
from gym import error, spaces


class UnityGymException(error.Error):
    """
    Any error related to the gym wrapper of ml-agents.
    """

    pass


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("gym_unity")


class UnityEnv(gym.Env):
    """
    Provides Gym wrapper for Unity Learning Environments.
    Multi-agent environments use lists for object types, as done here:
    https://github.com/openai/multiagent-particle-envs
    """

    def __init__(
        self,
        environment_filename: str,
        worker_id=0,
        use_visual=False,
        uint8_visual=False,
        multiagent=False,
        flatten_branched=False,
        no_graphics=False,
    ):
        """
        Environment initialization
        :param environment_filename: The UnityEnvironment path or file to be wrapped in the gym.
        :param worker_id: Worker number for environment.
        :param use_visual: Whether to use visual observation or vector observation.
        :param uint8_visual: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).
        :param multiagent: Whether to run in multi-agent mode (lists of obs, reward, done).
        :param flatten_branched: If True, turn branched discrete action spaces into a Discrete space rather than MultiDiscrete.
        :param no_graphics: Whether to run the Unity simulator in no-graphics mode
        """
        self._env = UnityEnvironment(
            environment_filename, worker_id, no_graphics=no_graphics
        )
        self.name = self._env.academy_name
        self.visual_obs = None
        self._current_state = None
        self._n_agents = None
        self._multiagent = multiagent
        self._flattener = None
        self.game_over = (
            False
        )  # Hidden flag used by Atari environments to determine if the game is over

        # Check brain configuration
        if len(self._env.brains) != 1:
            raise UnityGymException(
                "There can only be one brain in a UnityEnvironment "
                "if it is wrapped in a gym."
            )
        if len(self._env.external_brain_names) <= 0:
            raise UnityGymException(
                "There are not any external brain in the UnityEnvironment"
            )

        self.brain_name = self._env.external_brain_names[0]
        brain = self._env.brains[self.brain_name]

        if use_visual and brain.number_visual_observations == 0:
            raise UnityGymException(
                "`use_visual` was set to True, however there are no"
                " visual observations as part of this environment."
            )
        self.use_visual = brain.number_visual_observations >= 1 and use_visual

        if not use_visual and uint8_visual:
            logger.warning(
                "`uint8_visual was set to true, but visual observations are not in use. "
                "This setting will not have any effect."
            )
        else:
            self.uint8_visual = uint8_visual

        if brain.number_visual_observations > 1:
            logger.warning(
                "The environment contains more than one visual observation. "
                "Please note that only the first will be provided in the observation."
            )

        if brain.num_stacked_vector_observations != 1:
            raise UnityGymException(
                "There can only be one stacked vector observation in a UnityEnvironment "
                "if it is wrapped in a gym."
            )

        # Check for number of agents in scene.
        initial_info = self._env.reset()[self.brain_name]
        self._check_agents(len(initial_info.agents))

        # Set observation and action spaces
        if brain.vector_action_space_type == "discrete":
            if len(brain.vector_action_space_size) == 1:
                self._action_space = spaces.Discrete(brain.vector_action_space_size[0])
            else:
                if flatten_branched:
                    self._flattener = ActionFlattener(brain.vector_action_space_size)
                    self._action_space = self._flattener.action_space
                else:
                    self._action_space = spaces.MultiDiscrete(
                        brain.vector_action_space_size
                    )

        else:
            if flatten_branched:
                logger.warning(
                    "The environment has a non-discrete action space. It will "
                    "not be flattened."
                )
            high = np.array([1] * brain.vector_action_space_size[0])
            self._action_space = spaces.Box(-high, high, dtype=np.float32)
        high = np.array([np.inf] * brain.vector_observation_space_size)
        self.action_meanings = brain.vector_action_descriptions
        if self.use_visual:
            if brain.camera_resolutions[0]["blackAndWhite"]:
                depth = 1
            else:
                depth = 3
            self._observation_space = spaces.Box(
                0,
                1,
                dtype=np.float32,
                shape=(
                    brain.camera_resolutions[0]["height"],
                    brain.camera_resolutions[0]["width"],
                    depth,
                ),
            )
        else:
            self._observation_space = spaces.Box(-high, high, dtype=np.float32)

    def reset(self):
        """Resets the state of the environment and returns an initial observation.
        In the case of multi-agent environments, this is a list.
        Returns: observation (object/list): the initial observation of the
            space.
        """
        info = self._env.reset()[self.brain_name]
        n_agents = len(info.agents)
        self._check_agents(n_agents)
        self.game_over = False

        if not self._multiagent:
            obs, reward, done, info = self._single_step(info)
        else:
            obs, reward, done, info = self._multi_step(info)
        return obs

    def step(self, action):
        """Run one timestep of the environment's dynamics. When end of
        episode is reached, you are responsible for calling `reset()`
        to reset this environment's state.
        Accepts an action and returns a tuple (observation, reward, done, info).
        In the case of multi-agent environments, these are lists.
        Args:
            action (object/list): an action provided by the environment
        Returns:
            observation (object/list): agent's observation of the current environment
            reward (float/list) : amount of reward returned after previous action
            done (boolean/list): whether the episode has ended.
            info (dict): contains auxiliary diagnostic information, including BrainInfo.
        """

        # Use random actions for all other agents in environment.
        if self._multiagent:
            if not isinstance(action, list):
                raise UnityGymException(
                    "The environment was expecting `action` to be a list."
                )
            if len(action) != self._n_agents:
                raise UnityGymException(
                    "The environment was expecting a list of {} actions.".format(
                        self._n_agents
                    )
                )
            else:
                if self._flattener is not None:
                    # Action space is discrete and flattened - we expect a list of scalars
                    action = [self._flattener.lookup_action(_act) for _act in action]
                action = np.array(action)
        else:
            if self._flattener is not None:
                # Translate action into list
                action = self._flattener.lookup_action(action)

        info = self._env.step(action)[self.brain_name]
        n_agents = len(info.agents)
        self._check_agents(n_agents)
        self._current_state = info

        if not self._multiagent:
            obs, reward, done, info = self._single_step(info)
            self.game_over = done
        else:
            obs, reward, done, info = self._multi_step(info)
            self.game_over = all(done)
        return obs, reward, done, info

    def _single_step(self, info):
        if self.use_visual:
            visual_obs = info.visual_observations
            if isinstance(visual_obs, list):
                visual_obs = np.array(visual_obs)
            self.visual_obs = self._preprocess_single(visual_obs[0][0, :, :, :])
            default_observation = self.visual_obs
        else:
            default_observation = info.vector_observations[0, :]

        return (
            default_observation,
            info.rewards[0],
            info.local_done[0],
            {"text_observation": info.text_observations[0], "brain_info": info},
        )

    def _preprocess_single(self, single_visual_obs):
        if self.uint8_visual:
            return (255.0 * single_visual_obs).astype(np.uint8)
        else:
            return single_visual_obs

    def _multi_step(self, info):
        if self.use_visual:
            self.visual_obs = self._preprocess_multi(info.visual_observations)
            default_observation = self.visual_obs
        else:
            default_observation = info.vector_observations
        return (
            list(default_observation),
            info.rewards,
            info.local_done,
            {"text_observation": info.text_observations, "brain_info": info},
        )

    def _preprocess_multi(self, multiple_visual_obs):
        if self.uint8_visual:
            return [
                (255.0 * _visual_obs).astype(np.uint8)
                for _visual_obs in multiple_visual_obs
            ]
        else:
            return multiple_visual_obs

    def render(self, mode="rgb_array"):
        return self.visual_obs

    def close(self):
        """Override _close in your subclass to perform any necessary cleanup.
        Environments will automatically close() themselves when
        garbage collected or when the program exits.
        """
        self._env.close()

    def get_action_meanings(self):
        return self.action_meanings

    def seed(self, seed=None):
        """Sets the seed for this env's random number generator(s).
        Currently not implemented.
        """
        logger.warn("Could not seed environment %s", self.name)
        return

    def _check_agents(self, n_agents):
        if not self._multiagent and n_agents > 1:
            raise UnityGymException(
                "The environment was launched as a single-agent environment, however"
                "there is more than one agent in the scene."
            )
        elif self._multiagent and n_agents <= 1:
            raise UnityGymException(
                "The environment was launched as a mutli-agent environment, however"
                "there is only one agent in the scene."
            )
        if self._n_agents is None:
            self._n_agents = n_agents
            logger.info("{} agents within environment.".format(n_agents))
        elif self._n_agents != n_agents:
            raise UnityGymException(
                "The number of agents in the environment has changed since "
                "initialization. This is not supported."
            )

    @property
    def metadata(self):
        return {"render.modes": ["rgb_array"]}

    @property
    def reward_range(self):
        return -float("inf"), float("inf")

    @property
    def spec(self):
        return None

    @property
    def action_space(self):
        return self._action_space

    @property
    def observation_space(self):
        return self._observation_space

    @property
    def number_agents(self):
        return self._n_agents


class ActionFlattener:
    """
    Flattens branched discrete action spaces into single-branch discrete action spaces.
    """

    def __init__(self, branched_action_space):
        """
        Initialize the flattener.
        :param branched_action_space: A List containing the sizes of each branch of the action
        space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.
        """
        self._action_shape = branched_action_space
        self.action_lookup = self._create_lookup(self._action_shape)
        self.action_space = spaces.Discrete(len(self.action_lookup))

    @classmethod
    def _create_lookup(self, branched_action_space):
        """
        Creates a Dict that maps discrete actions (scalars) to branched actions (lists).
        Each key in the Dict maps to one unique set of branched actions, and each value
        contains the List of branched actions.
        """
        possible_vals = [range(_num) for _num in branched_action_space]
        all_actions = [list(_action) for _action in itertools.product(*possible_vals)]
        # Dict should be faster than List for large action spaces
        action_lookup = {
            _scalar: _action for (_scalar, _action) in enumerate(all_actions)
        }
        return action_lookup

    def lookup_action(self, action):
        """
        Convert a scalar discrete action into a unique set of branched actions.
        :param: action: A scalar value representing one of the discrete actions.
        :return: The List containing the branched actions.
        """
        return self.action_lookup[action]