ray/rllib/examples/env/windy_maze_env.py

import gym
from gym.spaces import Box, Discrete, Tuple
import logging
import random

from ray.rllib.env import MultiAgentEnv

logger = logging.getLogger(__name__)

# Agent has to traverse the maze from the starting position S -> F
# Observation space [x_pos, y_pos, wind_direction]
# Action space: stay still OR move in current wind direction
MAP_DATA = """
#########
#S      #
####### #
      # #
      # #
####### #
#F      #
#########"""


class WindyMazeEnv(gym.Env):
    def __init__(self, env_config):
        self.map = [m for m in MAP_DATA.split("\n") if m]
        self.x_dim = len(self.map)
        self.y_dim = len(self.map[0])
        logger.info("Loaded map {} {}".format(self.x_dim, self.y_dim))
        for x in range(self.x_dim):
            for y in range(self.y_dim):
                if self.map[x][y] == "S":
                    self.start_pos = (x, y)
                elif self.map[x][y] == "F":
                    self.end_pos = (x, y)
        logger.info("Start pos {} end pos {}".format(self.start_pos, self.end_pos))
        self.observation_space = Tuple(
            [
                Box(0, 100, shape=(2,)),  # (x, y)
                Discrete(4),  # wind direction (N, E, S, W)
            ]
        )
        self.action_space = Discrete(2)  # whether to move or not

    def reset(self):
        self.wind_direction = random.choice([0, 1, 2, 3])
        self.pos = self.start_pos
        self.num_steps = 0
        return [[self.pos[0], self.pos[1]], self.wind_direction]

    def step(self, action):
        if action == 1:
            self.pos = self._get_new_pos(self.pos, self.wind_direction)
        self.num_steps += 1
        self.wind_direction = random.choice([0, 1, 2, 3])
        at_goal = self.pos == self.end_pos
        done = at_goal or self.num_steps >= 200
        return (
            [[self.pos[0], self.pos[1]], self.wind_direction],
            100 * int(at_goal),
            done,
            {},
        )

    def _get_new_pos(self, pos, direction):
        if direction == 0:
            new_pos = (pos[0] - 1, pos[1])
        elif direction == 1:
            new_pos = (pos[0], pos[1] + 1)
        elif direction == 2:
            new_pos = (pos[0] + 1, pos[1])
        elif direction == 3:
            new_pos = (pos[0], pos[1] - 1)
        if (
            new_pos[0] >= 0
            and new_pos[0] < self.x_dim
            and new_pos[1] >= 0
            and new_pos[1] < self.y_dim
            and self.map[new_pos[0]][new_pos[1]] != "#"
        ):
            return new_pos
        else:
            return pos  # did not move


class HierarchicalWindyMazeEnv(MultiAgentEnv):
    def __init__(self, env_config):
        super().__init__()
        self._skip_env_checking = True
        self.flat_env = WindyMazeEnv(env_config)

    def reset(self):
        self.cur_obs = self.flat_env.reset()
        self.current_goal = None
        self.steps_remaining_at_level = None
        self.num_high_level_steps = 0
        # current low level agent id. This must be unique for each high level
        # step since agent ids cannot be reused.
        self.low_level_agent_id = "low_level_{}".format(self.num_high_level_steps)
        return {
            "high_level_agent": self.cur_obs,
        }

    def step(self, action_dict):
        assert len(action_dict) == 1, action_dict
        if "high_level_agent" in action_dict:
            return self._high_level_step(action_dict["high_level_agent"])
        else:
            return self._low_level_step(list(action_dict.values())[0])

    def _high_level_step(self, action):
        logger.debug("High level agent sets goal")
        self.current_goal = action
        self.steps_remaining_at_level = 25
        self.num_high_level_steps += 1
        self.low_level_agent_id = "low_level_{}".format(self.num_high_level_steps)
        obs = {self.low_level_agent_id: [self.cur_obs, self.current_goal]}
        rew = {self.low_level_agent_id: 0}
        done = {"__all__": False}
        return obs, rew, done, {}

    def _low_level_step(self, action):
        logger.debug("Low level agent step {}".format(action))
        self.steps_remaining_at_level -= 1
        cur_pos = tuple(self.cur_obs[0])
        goal_pos = self.flat_env._get_new_pos(cur_pos, self.current_goal)

        # Step in the actual env
        f_obs, f_rew, f_done, _ = self.flat_env.step(action)
        new_pos = tuple(f_obs[0])
        self.cur_obs = f_obs

        # Calculate low-level agent observation and reward
        obs = {self.low_level_agent_id: [f_obs, self.current_goal]}
        if new_pos != cur_pos:
            if new_pos == goal_pos:
                rew = {self.low_level_agent_id: 1}
            else:
                rew = {self.low_level_agent_id: -1}
        else:
            rew = {self.low_level_agent_id: 0}

        # Handle env termination & transitions back to higher level
        done = {"__all__": False}
        if f_done:
            done["__all__"] = True
            logger.debug("high level final reward {}".format(f_rew))
            rew["high_level_agent"] = f_rew
            obs["high_level_agent"] = f_obs
        elif self.steps_remaining_at_level == 0:
            done[self.low_level_agent_id] = True
            rew["high_level_agent"] = 0
            obs["high_level_agent"] = f_obs

        return obs, rew, done, {}