Environments¶

Any environment type provided by you to RLlib (e.g. a user-defined gym.Env class), is converted internally into the BaseEnv API, whose main methods are poll() and send_actions():

The BaseEnv API allows RLlib to support:

Vectorization of sub-environments (i.e. individual gym.Env instances, stacked to form a vector of envs) in order to batch the action computing model forward passes.
External simulators requiring async execution (e.g. envs that run on separate machines and independently request actions from a policy server).
Stepping through the individual sub-environments in parallel via pre-converting them into separate @ray.remote actors.
Multi-agent RL via dicts mapping agent IDs to observations/rewards/etc..

For example, if you provide a custom gym.Env class to RLlib, auto-conversion to BaseEnv goes as follows:

User provides a gym.Env -> _VectorizedGymEnv (is-a VectorEnv) -> BaseEnv

Here is a simple example:

# __rllib-custom-gym-env-begin__
import gym

import ray
from ray.rllib.agents import ppo


class SimpleCorridor(gym.Env):
    def __init__(self, config):
        self.end_pos = config["corridor_length"]
        self.cur_pos = 0
        self.action_space = gym.spaces.Discrete(2)  # right/left
        self.observation_space = gym.spaces.Discrete(self.end_pos)

    def reset(self):
        self.cur_pos = 0
        return self.cur_pos

    def step(self, action):
        if action == 0 and self.cur_pos > 0:  # move right (towards goal)
            self.cur_pos -= 1
        elif action == 1:  # move left (towards start)
            self.cur_pos += 1
        if self.cur_pos >= self.end_pos:
            return 0, 1.0, True, {}
        else:
            return self.cur_pos, -0.1, False, {}


ray.init()
config = {
    "env": SimpleCorridor,
    "env_config": {
        "corridor_length": 5,
    },
}

trainer = ppo.PPOTrainer(config=config)
for _ in range(3):
    print(trainer.train())
# __rllib-custom-gym-env-end__

However, you may also conveniently sub-class any of the other supported RLlib-specific environment types. The automated paths from those env types (or callables returning instances of those types) to an RLlib BaseEnv is as follows:

User provides a custom MultiAgentEnv (is-a gym.Env) -> VectorEnv -> BaseEnv
User uses a policy client (via an external simulator) -> ExternalEnv | ExternalMultiAgentEnv -> BaseEnv
User provides a custom VectorEnv -> BaseEnv
User provides a custom BaseEnv -> do nothing

Ray v2.0.0.dev0

Environments¶

Environment API Reference¶