ray/rllib/examples/env/simple_rpg.py

import gym
from gym.spaces import Discrete, Box, Dict

from ray.rllib.utils.spaces.repeated import Repeated

# Constraints on the Repeated space.
MAX_PLAYERS = 4
MAX_ITEMS = 7
MAX_EFFECTS = 2


class SimpleRPG(gym.Env):
    """Example of a custom env with a complex, structured observation.

    The observation is a list of players, each of which is a Dict of
    attributes, and may further hold a list of items (categorical space).

    Note that the env doesn't train, it's just a dummy example to show how to
    use spaces.Repeated in a custom model (see CustomRPGModel below).
    """

    def __init__(self, config):
        self.cur_pos = 0
        self.action_space = Discrete(4)

        # Represents an item.
        self.item_space = Discrete(5)

        # Represents an effect on the player.
        self.effect_space = Box(9000, 9999, shape=(4,))

        # Represents a player.
        self.player_space = Dict(
            {
                "location": Box(-100, 100, shape=(2,)),
                "status": Box(-1, 1, shape=(10,)),
                "items": Repeated(self.item_space, max_len=MAX_ITEMS),
                "effects": Repeated(self.effect_space, max_len=MAX_EFFECTS),
            }
        )

        # Observation is a list of players.
        self.observation_space = Repeated(self.player_space, max_len=MAX_PLAYERS)

    def reset(self):
        return self.observation_space.sample()

    def step(self, action):
        return self.observation_space.sample(), 1, True, {}