ray/rllib/examples/custom_env.py

"""Example of a custom gym environment and model. Run this for a demo.

This example shows:
  - using a custom environment
  - using a custom model
  - using Tune for grid search

You can visualize experiment results in ~/ray_results using TensorBoard.
"""

import numpy as np
import gym
from ray.rllib.models import ModelCatalog
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.tf.fcnet_v2 import FullyConnectedNetwork
from gym.spaces import Discrete, Box

import ray
from ray import tune
from ray.rllib.utils import try_import_tf
from ray.tune import grid_search

tf = try_import_tf()


class SimpleCorridor(gym.Env):
    """Example of a custom env in which you have to walk down a corridor.

    You can configure the length of the corridor via the env config."""

    def __init__(self, config):
        self.end_pos = config["corridor_length"]
        self.cur_pos = 0
        self.action_space = Discrete(2)
        self.observation_space = Box(
            0.0, self.end_pos, shape=(1, ), dtype=np.float32)

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

    def step(self, action):
        assert action in [0, 1], action
        if action == 0 and self.cur_pos > 0:
            self.cur_pos -= 1
        elif action == 1:
            self.cur_pos += 1
        done = self.cur_pos >= self.end_pos
        return [self.cur_pos], 1 if done else 0, done, {}


class CustomModel(TFModelV2):
    """Example of a custom model that just delegates to a fc-net."""

    def __init__(self, obs_space, action_space, num_outputs, model_config,
                 name):
        super(CustomModel, self).__init__(obs_space, action_space, num_outputs,
                                          model_config, name)
        self.model = FullyConnectedNetwork(obs_space, action_space,
                                           num_outputs, model_config, name)
        self.register_variables(self.model.variables())

    def forward(self, input_dict, state, seq_lens):
        return self.model.forward(input_dict, state, seq_lens)

    def value_function(self):
        return self.model.value_function()


if __name__ == "__main__":
    # Can also register the env creator function explicitly with:
    # register_env("corridor", lambda config: SimpleCorridor(config))
    ray.init()
    ModelCatalog.register_custom_model("my_model", CustomModel)
    tune.run(
        "PPO",
        stop={
            "timesteps_total": 10000,
        },
        config={
            "env": SimpleCorridor,  # or "corridor" if registered above
            "model": {
                "custom_model": "my_model",
            },
            "vf_share_layers": True,
            "lr": grid_search([1e-2, 1e-4, 1e-6]),  # try different lrs
            "num_workers": 1,  # parallelism
            "env_config": {
                "corridor_length": 5,
            },
        },
    )