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

import itertools
import numpy as np
from typing import Any, Dict, List, Optional, Tuple, Union

import gym
from gym import error, spaces

from mlagents_envs.base_env import BaseEnv
from mlagents_envs.base_env import DecisionSteps, TerminalSteps
from mlagents_envs import logging_util


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

    pass


logger = logging_util.get_logger(__name__)
logging_util.set_log_level(logging_util.INFO)

GymStepResult = Tuple[np.ndarray, float, bool, Dict]


class UnityToGymWrapper(gym.Env):
    """
    Provides Gym wrapper for Unity Learning Environments.
    """

    def __init__(
        self,
        unity_env: BaseEnv,
        use_visual: bool = False,
        uint8_visual: bool = False,
        flatten_branched: bool = False,
        allow_multiple_visual_obs: bool = False,
    ):
        """
        Environment initialization
        :param unity_env: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes.
        :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 flatten_branched: If True, turn branched discrete action spaces into a Discrete space rather than
            MultiDiscrete.
        :param allow_multiple_visual_obs: If True, return a list of visual observations instead of only one.
        """
        self._env = unity_env

        # Take a single step so that the brain information will be sent over
        if not self._env.get_behavior_names():
            self._env.step()

        self.visual_obs = None
        self._n_agents = -1

        # Save the step result from the last time all Agents requested decisions.
        self._previous_decision_step: DecisionSteps = None
        self._flattener = None
        # Hidden flag used by Atari environments to determine if the game is over
        self.game_over = False
        self._allow_multiple_visual_obs = allow_multiple_visual_obs

        # Check brain configuration
        if len(self._env.get_behavior_names()) != 1:
            raise UnityGymException(
                "There can only be one behavior in a UnityEnvironment "
                "if it is wrapped in a gym."
            )

        self.name = self._env.get_behavior_names()[0]
        self.group_spec = self._env.get_behavior_spec(self.name)

        if use_visual and self._get_n_vis_obs() == 0:
            raise UnityGymException(
                "`use_visual` was set to True, however there are no"
                " visual observations as part of this environment."
            )
        self.use_visual = self._get_n_vis_obs() >= 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 self._get_n_vis_obs() > 1 and not self._allow_multiple_visual_obs:
            logger.warning(
                "The environment contains more than one visual observation. "
                "You must define allow_multiple_visual_obs=True to received them all. "
                "Otherwise, please note that only the first will be provided in the observation."
            )

        # Check for number of agents in scene.
        self._env.reset()
        decision_steps, _ = self._env.get_steps(self.name)
        self._check_agents(len(decision_steps))
        self._previous_decision_step = decision_steps

        # Set observation and action spaces
        if self.group_spec.is_action_discrete():
            branches = self.group_spec.discrete_action_branches
            if self.group_spec.action_shape == 1:
                self._action_space = spaces.Discrete(branches[0])
            else:
                if flatten_branched:
                    self._flattener = ActionFlattener(branches)
                    self._action_space = self._flattener.action_space
                else:
                    self._action_space = spaces.MultiDiscrete(branches)

        else:
            if flatten_branched:
                logger.warning(
                    "The environment has a non-discrete action space. It will "
                    "not be flattened."
                )
            high = np.array([1] * self.group_spec.action_shape)
            self._action_space = spaces.Box(-high, high, dtype=np.float32)
        high = np.array([np.inf] * self._get_vec_obs_size())
        if self.use_visual:
            shape = self._get_vis_obs_shape()
            if uint8_visual:
                self._observation_space = spaces.Box(
                    0, 255, dtype=np.uint8, shape=shape
                )
            else:
                self._observation_space = spaces.Box(
                    0, 1, dtype=np.float32, shape=shape
                )

        else:
            self._observation_space = spaces.Box(-high, high, dtype=np.float32)

    def reset(self) -> Union[List[np.ndarray], np.ndarray]:
        """Resets the state of the environment and returns an initial observation.
        Returns: observation (object/list): the initial observation of the
        space.
        """
        self._env.reset()
        decision_step, _ = self._env.get_steps(self.name)
        n_agents = len(decision_step)
        self._check_agents(n_agents)
        self.game_over = False

        res: GymStepResult = self._single_step(decision_step)
        return res[0]

    def step(self, action: List[Any]) -> GymStepResult:
        """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).
        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.
        """
        if self._flattener is not None:
            # Translate action into list
            action = self._flattener.lookup_action(action)

        spec = self.group_spec
        action = np.array(action).reshape((1, spec.action_size))
        self._env.set_actions(self.name, action)

        self._env.step()
        decision_step, terminal_step = self._env.get_steps(self.name)
        if len(terminal_step) != 0:
            # The agent is done
            self.game_over = True
            return self._single_step(terminal_step)
        else:
            return self._single_step(decision_step)

    def _single_step(self, info: Union[DecisionSteps, TerminalSteps]) -> GymStepResult:
        if self.use_visual:
            visual_obs = self._get_vis_obs_list(info)

            if self._allow_multiple_visual_obs:
                visual_obs_list = []
                for obs in visual_obs:
                    visual_obs_list.append(self._preprocess_single(obs[0]))
                self.visual_obs = visual_obs_list
            else:
                self.visual_obs = self._preprocess_single(visual_obs[0][0])

            default_observation = self.visual_obs
        elif self._get_vec_obs_size() > 0:
            default_observation = self._get_vector_obs(info)[0, :]
        else:
            raise UnityGymException(
                "The Agent does not have vector observations and the environment was not setup "
                + "to use visual observations."
            )
        done = isinstance(info, TerminalSteps)

        return (default_observation, info.reward[0], done, {"step": info})

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

    def _get_n_vis_obs(self) -> int:
        result = 0
        for shape in self.group_spec.observation_shapes:
            if len(shape) == 3:
                result += 1
        return result

    def _get_vis_obs_shape(self) -> Optional[Tuple]:
        for shape in self.group_spec.observation_shapes:
            if len(shape) == 3:
                return shape
        return None

    def _get_vis_obs_list(
        self, step_result: Union[DecisionSteps, TerminalSteps]
    ) -> List[np.ndarray]:
        result: List[np.ndarray] = []
        for obs in step_result.obs:
            if len(obs.shape) == 4:
                result.append(obs)
        return result

    def _get_vector_obs(
        self, step_result: Union[DecisionSteps, TerminalSteps]
    ) -> np.ndarray:
        result: List[np.ndarray] = []
        for obs in step_result.obs:
            if len(obs.shape) == 2:
                result.append(obs)
        return np.concatenate(result, axis=1)

    def _get_vec_obs_size(self) -> int:
        result = 0
        for shape in self.group_spec.observation_shapes:
            if len(shape) == 1:
                result += shape[0]
        return result

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

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

    def _check_agents(self, n_agents: int) -> None:
        if self._n_agents > 1:
            raise UnityGymException(
                "There can only be one Agent in the environment but {n_agents} were detected."
            )

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

    @property
    def reward_range(self) -> Tuple[float, float]:
        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]