predictions.py 7.63 KB
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"""
Collection of environment-specific PredictionBuilder.
"""

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import numpy as np

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from flatland.core.env_prediction_builder import PredictionBuilder
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from flatland.core.grid.grid4_utils import get_new_position
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from flatland.envs.rail_env import RailEnvActions
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class DummyPredictorForRailEnv(PredictionBuilder):
    """
    DummyPredictorForRailEnv object.

    This object returns predictions for agents in the RailEnv environment.
    The prediction acts as if no other agent is in the environment and always takes the forward action.
    """

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    def get(self, custom_args=None, handle=None):
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        """
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        Called whenever get_many in the observation build is called.
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        Parameters
        -------
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        custom_args: dict
            Not used in this dummy implementation.
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        handle : int (optional)
            Handle of the agent for which to compute the observation vector.

        Returns
        -------
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        np.array
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            Returns a dictionary indexed by the agent handle and for each agent a vector of (max_depth + 1)x5 elements:
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            - time_offset
            - position axis 0
            - position axis 1
            - direction
            - action taken to come here
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            The prediction at 0 is the current position, direction etc.

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        """
        agents = self.env.agents
        if handle:
            agents = [self.env.agents[handle]]

        prediction_dict = {}
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        for agent in agents:
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            action_priorities = [RailEnvActions.MOVE_FORWARD, RailEnvActions.MOVE_LEFT, RailEnvActions.MOVE_RIGHT]
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            _agent_initial_position = agent.position
            _agent_initial_direction = agent.direction
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            prediction = np.zeros(shape=(self.max_depth + 1, 5))
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            prediction[0] = [0, *_agent_initial_position, _agent_initial_direction, 0]
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            for index in range(1, self.max_depth + 1):
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                action_done = False
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                # if we're at the target, stop moving...
                if agent.position == agent.target:
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                    prediction[index] = [index, *agent.target, agent.direction, RailEnvActions.STOP_MOVING]
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                    continue
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                for action in action_priorities:
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                    cell_isFree, new_cell_isValid, new_direction, new_position, transition_isValid = \
                        self.env._check_action_on_agent(action, agent)
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                    if all([new_cell_isValid, transition_isValid]):
                        # move and change direction to face the new_direction that was
                        # performed
                        agent.position = new_position
                        agent.direction = new_direction
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                        prediction[index] = [index, *new_position, new_direction, action]
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                        action_done = True
                        break
                if not action_done:
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                    raise Exception("Cannot move further. Something is wrong")
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            prediction_dict[agent.handle] = prediction
            agent.position = _agent_initial_position
            agent.direction = _agent_initial_direction
        return prediction_dict
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class ShortestPathPredictorForRailEnv(PredictionBuilder):
    """
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    ShortestPathPredictorForRailEnv object.
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    This object returns shortest-path predictions for agents in the RailEnv environment.
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    The prediction acts as if no other agent is in the environment and always takes the forward action.
    """

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    def __init__(self, max_depth=20):
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        # Initialize with depth 20
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        self.max_depth = max_depth

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    def get(self, custom_args=None, handle=None):
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        """
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        Called whenever get_many in the observation build is called.
        Requires distance_map to extract the shortest path.
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        Parameters
        -------
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        custom_args: dict
            - distance_map : dict
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        handle : int (optional)
            Handle of the agent for which to compute the observation vector.

        Returns
        -------
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        np.array
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            Returns a dictionary indexed by the agent handle and for each agent a vector of (max_depth + 1)x5 elements:
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            - time_offset
            - position axis 0
            - position axis 1
            - direction
            - action taken to come here
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            The prediction at 0 is the current position, direction etc.
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        """
        agents = self.env.agents
        if handle:
            agents = [self.env.agents[handle]]
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        assert custom_args is not None
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        distance_map = custom_args.get('distance_map')
        assert distance_map is not None
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        prediction_dict = {}
        for agent in agents:
            _agent_initial_position = agent.position
            _agent_initial_direction = agent.direction
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            agent_speed = agent.speed_data["speed"]
            times_per_cell = int(np.reciprocal(agent_speed))
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            prediction = np.zeros(shape=(self.max_depth + 1, 5))
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            prediction[0] = [0, *_agent_initial_position, _agent_initial_direction, 0]
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            new_direction = _agent_initial_direction
            new_position = _agent_initial_position
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            visited = set()
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            for index in range(1, self.max_depth + 1):
                # if we're at the target, stop moving...
                if agent.position == agent.target:
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                    prediction[index] = [index, *agent.target, agent.direction, RailEnvActions.STOP_MOVING]
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                    visited.add((agent.position[0], agent.position[1], agent.direction))
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                    continue
                if not agent.moving:
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                    prediction[index] = [index, *agent.position, agent.direction, RailEnvActions.STOP_MOVING]
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                    visited.add((agent.position[0], agent.position[1], agent.direction))
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                    continue
                # Take shortest possible path
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                cell_transitions = self.env.rail.get_transitions(*agent.position, agent.direction)
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                if np.sum(cell_transitions) == 1 and index % times_per_cell == 0:
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                    new_direction = np.argmax(cell_transitions)
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                    new_position = get_new_position(agent.position, new_direction)
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                elif np.sum(cell_transitions) > 1 and index % times_per_cell == 0:
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                    min_dist = np.inf
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                    no_dist_found = True
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                    for direction in range(4):
                        if cell_transitions[direction] == 1:
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                            neighbour_cell = get_new_position(agent.position, direction)
                            target_dist = distance_map[agent.handle, neighbour_cell[0], neighbour_cell[1], direction]
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                            if target_dist < min_dist or no_dist_found:
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                                min_dist = target_dist
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                                new_direction = direction
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                                no_dist_found = False
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                    new_position = get_new_position(agent.position, new_direction)
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                elif index % times_per_cell == 0:
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                    raise Exception("No transition possible {}".format(cell_transitions))

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                # update the agent's position and direction
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                agent.position = new_position
                agent.direction = new_direction
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                # prediction is ready
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                prediction[index] = [index, *new_position, new_direction, 0]
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                visited.add((new_position[0], new_position[1], new_direction))
            self.env.dev_pred_dict[agent.handle] = visited
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            prediction_dict[agent.handle] = prediction
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            # cleanup: reset initial position
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            agent.position = _agent_initial_position
            agent.direction = _agent_initial_direction

        return prediction_dict