rail_env.py 17.3 KB
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"""
Definition of the RailEnv environment and related level-generation functions.

Generator functions are functions that take width, height and num_resets as arguments and return
a GridTransitionMap object.
"""
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# TODO:  _ this is a global method --> utils or remove later
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from enum import IntEnum

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import msgpack
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import numpy as np
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from flatland.core.env import Environment
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from flatland.envs.agent_utils import EnvAgentStatic, EnvAgent
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from flatland.envs.env_utils import get_new_position
from flatland.envs.generators import random_rail_generator
from flatland.envs.observations import TreeObsForRailEnv

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class RailEnvActions(IntEnum):
    DO_NOTHING = 0
    MOVE_LEFT = 1
    MOVE_FORWARD = 2
    MOVE_RIGHT = 3
    STOP_MOVING = 4

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class RailEnv(Environment):
    """
    RailEnv environment class.

    RailEnv is an environment inspired by a (simplified version of) a rail
    network, in which agents (trains) have to navigate to their target
    locations in the shortest time possible, while at the same time cooperating
    to avoid bottlenecks.

    The valid actions in the environment are:
        0: do nothing
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        1: turn left and move to the next cell; if the agent was not moving, movement is started
        2: move to the next cell in front of the agent; if the agent was not moving, movement is started
        3: turn right and move to the next cell; if the agent was not moving, movement is started
        4: stop moving
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    Moving forward in a dead-end cell makes the agent turn 180 degrees and step
    to the cell it came from.

    The actions of the agents are executed in order of their handle to prevent
    deadlocks and to allow them to learn relative priorities.

    TODO: WRITE ABOUT THE REWARD FUNCTION, and possibly allow for alpha and
    beta to be passed as parameters to __init__().
    """

    def __init__(self,
                 width,
                 height,
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                 rail_generator=random_rail_generator(),
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                 number_of_agents=1,
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                 obs_builder_object=TreeObsForRailEnv(max_depth=2),
                 ):
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        """
        Environment init.

        Parameters
        -------
        rail_generator : function
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            The rail_generator function is a function that takes the width,
            height and agents handles of a  rail environment, along with the number of times
            the env has been reset, and returns a GridTransitionMap object and a list of
            starting positions, targets, and initial orientations for agent handle.
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            Implemented functions are:
                random_rail_generator : generate a random rail of given size
                rail_from_GridTransitionMap_generator(rail_map) : generate a rail from
                                        a GridTransitionMap object
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                rail_from_manual_sp ecifications_generator(rail_spec) : generate a rail from
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                                        a rail specifications array
                TODO: generate_rail_from_saved_list or from list of ndarray bitmaps ---
        width : int
            The width of the rail map. Potentially in the future,
            a range of widths to sample from.
        height : int
            The height of the rail map. Potentially in the future,
            a range of heights to sample from.
        number_of_agents : int
            Number of agents to spawn on the map. Potentially in the future,
            a range of number of agents to sample from.
        obs_builder_object: ObservationBuilder object
            ObservationBuilder-derived object that takes builds observation
            vectors for each agent.
        """

        self.rail_generator = rail_generator
        self.rail = None
        self.width = width
        self.height = height

        self.obs_builder = obs_builder_object
        self.obs_builder._set_env(self)

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        self.action_space = [1]
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        self.observation_space = self.obs_builder.observation_space  # updated on resets?
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        self.rewards = [0] * number_of_agents
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        self.done = False

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        self.dones = dict.fromkeys(list(range(number_of_agents)) + ["__all__"], False)

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        self.obs_dict = {}
        self.rewards_dict = {}
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        self.dev_obs_dict = {}
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        self.agents = [None] * number_of_agents  # live agents
        self.agents_static = [None] * number_of_agents  # static agent information
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        self.num_resets = 0
        self.reset()
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        self.num_resets = 0  # yes, set it to zero again!
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        self.valid_positions = None

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    # no more agent_handles
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    def get_agent_handles(self):
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        return range(self.get_num_agents())

    def get_num_agents(self, static=True):
        if static:
            return len(self.agents_static)
        else:
            return len(self.agents)
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    def add_agent_static(self, agent_static):
        """ Add static info for a single agent.
            Returns the index of the new agent.
        """
        self.agents_static.append(agent_static)
        return len(self.agents_static) - 1

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    def restart_agents(self):
        """ Reset the agents to their starting positions defined in agents_static
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        """
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        self.agents = EnvAgent.list_from_static(self.agents_static)

    def reset(self, regen_rail=True, replace_agents=True):
        """ if regen_rail then regenerate the rails.
            if replace_agents then regenerate the agents static.
            Relies on the rail_generator returning agent_static lists (pos, dir, target)
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        """
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        tRailAgents = self.rail_generator(self.width, self.height, self.get_num_agents(), self.num_resets)
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        if regen_rail or self.rail is None:
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            self.rail = tRailAgents[0]
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        if replace_agents:
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            self.agents_static = EnvAgentStatic.from_lists(*tRailAgents[1:4])

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        self.restart_agents()
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        self.num_resets += 1

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        # TODO perhaps dones should be part of each agent.
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        self.dones = dict.fromkeys(list(range(self.get_num_agents())) + ["__all__"], False)
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        # Reset the state of the observation builder with the new environment
        self.obs_builder.reset()
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        self.observation_space = self.obs_builder.observation_space  # <-- change on reset?
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        # Return the new observation vectors for each agent
        return self._get_observations()

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    def step(self, action_dict_):
        action_dict = action_dict_.copy()

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        alpha = 1.0
        beta = 1.0

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        invalid_action_penalty = 0  # previously -2; GIACOMO: we decided that invalid actions will carry no penalty
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        step_penalty = -1 * alpha
        global_reward = 1 * beta
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        stop_penalty = 0  # penalty for stopping a moving agent
        start_penalty = 0  # penalty for starting a stopped agent
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        # Reset the step rewards
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        self.rewards_dict = dict()
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        for iAgent in range(self.get_num_agents()):
            self.rewards_dict[iAgent] = 0
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        if self.dones["__all__"]:
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            self.rewards_dict = [r + global_reward for r in self.rewards_dict]
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            return self._get_observations(), self.rewards_dict, self.dones, {}

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        # for i in range(len(self.agents_handles)):
        for iAgent in range(self.get_num_agents()):
            agent = self.agents[iAgent]
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            agent.speed_data['speed']=0.5
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            if self.dones[iAgent]:  # this agent has already completed...
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                continue
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            if np.equal(agent.position, agent.target).all():
                self.dones[iAgent] = True
            else:
                self.rewards_dict[iAgent] += step_penalty * agent.speed_data['speed']

            if iAgent not in action_dict:  # no action has been supplied for this agent
                action_dict[iAgent] = RailEnvActions.DO_NOTHING

            if action_dict[iAgent] < 0 or action_dict[iAgent] > len(RailEnvActions):
                print('ERROR: illegal action=', action_dict[iAgent],
                      'for agent with index=', iAgent,
                      '"DO NOTHING" will be executed instead')
                action_dict[iAgent] = RailEnvActions.DO_NOTHING

            action = action_dict[iAgent]
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            if action == RailEnvActions.DO_NOTHING and agent.moving:
                # Keep moving
                action = RailEnvActions.MOVE_FORWARD

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            if action == RailEnvActions.STOP_MOVING and agent.moving and agent.speed_data['position_fraction'] < 0.01:
                # Only allow halting an agent on entering new cells.
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                agent.moving = False
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                self.rewards_dict[iAgent] += stop_penalty

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            if not agent.moving and not (action == RailEnvActions.DO_NOTHING or action == RailEnvActions.STOP_MOVING):
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                # Only allow agent to start moving by pressing forward.
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                agent.moving = True
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                self.rewards_dict[iAgent] += start_penalty
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            # Now perform a movement.
            # If the agent is in an initial position within a new cell (agent.speed_data['position_fraction']<eps)
            #   store the desired action in `transition_action_on_cellexit' (only if the desired transition is
            #   allowed! otherwise DO_NOTHING!)
            # Then in any case (if agent.moving) and the `transition_action_on_cellexit' is valid, increment the
            #   position_fraction by the speed of the agent   (regardless of action taken, as long as no
            #   STOP_MOVING, but that makes agent.moving=False)
            # If the new position fraction is >= 1, reset to 0, and perform the stored
            #   transition_action_on_cellexit

            # If the agent can make an action
            action_selected = False
            if agent.speed_data['position_fraction'] < 0.01:
                if action != RailEnvActions.DO_NOTHING and action != RailEnvActions.STOP_MOVING:
                    cell_isFree, new_cell_isValid, new_direction, new_position, transition_isValid = \
                        self._check_action_on_agent(action, agent)

                    if all([new_cell_isValid, transition_isValid, cell_isFree]):
                        agent.speed_data['transition_action_on_cellexit'] = action
                        action_selected = True

                    else:
                        # But, if the chosen invalid action was LEFT/RIGHT, and the agent is moving,
                        # try to keep moving forward!
                        if (action == RailEnvActions.MOVE_LEFT or action == RailEnvActions.MOVE_RIGHT) and agent.moving:
                            cell_isFree, new_cell_isValid, new_direction, new_position, transition_isValid = \
                                self._check_action_on_agent(RailEnvActions.MOVE_FORWARD, agent)

                            if all([new_cell_isValid, transition_isValid, cell_isFree]):
                                agent.speed_data['transition_action_on_cellexit'] = RailEnvActions.MOVE_FORWARD
                                action_selected = True

                            else:
                                # TODO: an invalid action was chosen after entering the cell. The agent cannot move.
                                self.rewards_dict[iAgent] += invalid_action_penalty
                                agent.moving = False
                                self.rewards_dict[iAgent] += stop_penalty
                                continue
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                        else:
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                            # TODO: an invalid action was chosen after entering the cell. The agent cannot move.
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                            self.rewards_dict[iAgent] += invalid_action_penalty
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                            agent.moving = False
                            self.rewards_dict[iAgent] += stop_penalty
                            continue
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            if agent.moving and (action_selected or agent.speed_data['position_fraction'] >= 0.01):
                agent.speed_data['position_fraction'] += agent.speed_data['speed']
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            if agent.speed_data['position_fraction'] >= 1.0:
                agent.speed_data['position_fraction'] = 0.0

                # Perform stored action to transition to the next cell

                # Now 'transition_action_on_cellexit' will be guaranteed to be valid; it was checked on entering
                # the cell
                cell_isFree, new_cell_isValid, new_direction, new_position, transition_isValid = \
                    self._check_action_on_agent(agent.speed_data['transition_action_on_cellexit'], agent)
                agent.old_direction = agent.direction
                agent.old_position = agent.position
                agent.position = new_position
                agent.direction = new_direction
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        # Check for end of episode + add global reward to all rewards!
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        if np.all([np.array_equal(agent2.position, agent2.target) for agent2 in self.agents]):
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            self.dones["__all__"] = True
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            self.rewards_dict = [0 * r + global_reward for r in self.rewards_dict]
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        return self._get_observations(), self.rewards_dict, self.dones, {}

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    def _check_action_on_agent(self, action, agent):
        # compute number of possible transitions in the current
        # cell used to check for invalid actions
        new_direction, transition_isValid = self.check_action(agent, action)
        new_position = get_new_position(agent.position, new_direction)
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        # Is it a legal move?
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        # 1) transition allows the new_direction in the cell,
        # 2) the new cell is not empty (case 0),
        # 3) the cell is free, i.e., no agent is currently in that cell
        new_cell_isValid = (
            np.array_equal(  # Check the new position is still in the grid
                new_position,
                np.clip(new_position, [0, 0], [self.height - 1, self.width - 1]))
            and  # check the new position has some transitions (ie is not an empty cell)
            self.rail.get_transitions(new_position) > 0)
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        # If transition validity hasn't been checked yet.
        if transition_isValid is None:
            transition_isValid = self.rail.get_transition(
                (*agent.position, agent.direction),
                new_direction)
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        # Check the new position is not the same as any of the existing agent positions
        # (including itself, for simplicity, since it is moving)
        cell_isFree = not np.any(
            np.equal(new_position, [agent2.position for agent2 in self.agents]).all(1))
        return cell_isFree, new_cell_isValid, new_direction, new_position, transition_isValid

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    def check_action(self, agent, action):
        transition_isValid = None
        possible_transitions = self.rail.get_transitions((*agent.position, agent.direction))
        num_transitions = np.count_nonzero(possible_transitions)

        new_direction = agent.direction
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        if action == RailEnvActions.MOVE_LEFT:
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            new_direction = agent.direction - 1
            if num_transitions <= 1:
                transition_isValid = False

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        elif action == RailEnvActions.MOVE_RIGHT:
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            new_direction = agent.direction + 1
            if num_transitions <= 1:
                transition_isValid = False

        new_direction %= 4

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        if action == RailEnvActions.MOVE_FORWARD:
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            if num_transitions == 1:
                # - dead-end, straight line or curved line;
                # new_direction will be the only valid transition
                # - take only available transition
                new_direction = np.argmax(possible_transitions)
                transition_isValid = True
        return new_direction, transition_isValid

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    def _get_observations(self):
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        self.obs_dict = self.obs_builder.get_many(list(range(self.get_num_agents())))
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        return self.obs_dict
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    def get_full_state_msg(self):
        grid_data = self.rail.grid.tolist()
        agent_static_data = [agent.to_list() for agent in self.agents_static]
        agent_data = [agent.to_list() for agent in self.agents]
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        msgpack.packb(grid_data)
        msgpack.packb(agent_data)
        msgpack.packb(agent_static_data)

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        msg_data = {
            "grid": grid_data,
            "agents_static": agent_static_data,
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            "agents": agent_data}
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        return msgpack.packb(msg_data, use_bin_type=True)

    def get_agent_state_msg(self):
        agent_data = [agent.to_list() for agent in self.agents]
        msg_data = {
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            "agents": agent_data}
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        return msgpack.packb(msg_data, use_bin_type=True)

    def set_full_state_msg(self, msg_data):
        data = msgpack.unpackb(msg_data, use_list=False)
        self.rail.grid = np.array(data[b"grid"])
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        # agents are always reset as not moving
        self.agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data[b"agents_static"]]
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        self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4]) for d in data[b"agents"]]
        # setup with loaded data
        self.height, self.width = self.rail.grid.shape
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        self.rail.height = self.height
        self.rail.width = self.width
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        self.dones = dict.fromkeys(list(range(self.get_num_agents())) + ["__all__"], False)

    def save(self, filename):
        with open(filename, "wb") as file_out:
            file_out.write(self.get_full_state_msg())

    def load(self, filename):
        with open(filename, "rb") as file_in:
            load_data = file_in.read()
            self.set_full_state_msg(load_data)
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    def load_resource(self, package, resource):
        from importlib_resources import read_binary
        load_data = read_binary(package, resource)
        self.set_full_state_msg(load_data)