mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
"""Example of a custom gym environment and model. Run this for a demo.
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This example shows:
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- using a custom environment
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- using a custom model
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- using Tune for grid search
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You can visualize experiment results in ~/ray_results using TensorBoard.
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"""
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import argparse
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import gym
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from gym.spaces import Discrete, Box
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import numpy as np
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import os
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import ray
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from ray import tune
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from ray.tune import grid_search
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from ray.rllib.models import ModelCatalog
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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from ray.rllib.utils.test_utils import check_learning_achieved
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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parser = argparse.ArgumentParser()
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parser.add_argument("--run", type=str, default="PPO")
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parser.add_argument("--torch", action="store_true")
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=50)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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parser.add_argument("--stop-reward", type=float, default=0.1)
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class SimpleCorridor(gym.Env):
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"""Example of a custom env in which you have to walk down a corridor.
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You can configure the length of the corridor via the env config."""
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def __init__(self, config):
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self.end_pos = config["corridor_length"]
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self.cur_pos = 0
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self.action_space = Discrete(2)
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self.observation_space = Box(
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0.0, self.end_pos, shape=(1, ), dtype=np.float32)
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def reset(self):
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self.cur_pos = 0
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return [self.cur_pos]
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def step(self, action):
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assert action in [0, 1], action
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if action == 0 and self.cur_pos > 0:
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self.cur_pos -= 1
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elif action == 1:
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self.cur_pos += 1
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done = self.cur_pos >= self.end_pos
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return [self.cur_pos], 1.0 if done else -0.1, done, {}
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class CustomModel(TFModelV2):
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"""Example of a keras custom model that just delegates to an fc-net."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super(CustomModel, self).__init__(obs_space, action_space, num_outputs,
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model_config, name)
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self.model = FullyConnectedNetwork(obs_space, action_space,
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num_outputs, model_config, name)
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def forward(self, input_dict, state, seq_lens):
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return self.model.forward(input_dict, state, seq_lens)
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def value_function(self):
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return self.model.value_function()
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class TorchCustomModel(TorchModelV2, nn.Module):
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"""Example of a PyTorch custom model that just delegates to a fc-net."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
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model_config, name)
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nn.Module.__init__(self)
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self.torch_sub_model = TorchFC(obs_space, action_space, num_outputs,
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model_config, name)
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def forward(self, input_dict, state, seq_lens):
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input_dict["obs"] = input_dict["obs"].float()
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fc_out, _ = self.torch_sub_model(input_dict, state, seq_lens)
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return fc_out, []
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def value_function(self):
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return torch.reshape(self.torch_sub_model.value_function(), [-1])
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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# Can also register the env creator function explicitly with:
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# register_env("corridor", lambda config: SimpleCorridor(config))
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ModelCatalog.register_custom_model(
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"my_model", TorchCustomModel if args.torch else CustomModel)
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config = {
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"env": SimpleCorridor, # or "corridor" if registered above
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"env_config": {
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"corridor_length": 5,
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},
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"model": {
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"custom_model": "my_model",
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"vf_share_layers": True,
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},
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"lr": grid_search([1e-2, 1e-4, 1e-6]), # try different lrs
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"num_workers": 1, # parallelism
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"framework": "torch" if args.torch else "tf",
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.run(args.run, config=config, stop=stop)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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