mirror of
https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
92 lines
2.8 KiB
Python
92 lines
2.8 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 numpy as np
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import gym
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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_v2 import FullyConnectedNetwork
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from gym.spaces import Discrete, Box
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import ray
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from ray import tune
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from ray.rllib.utils import try_import_tf
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from ray.tune import grid_search
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tf = try_import_tf()
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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 if done else 0, done, {}
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class CustomModel(TFModelV2):
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"""Example of a 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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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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self.register_variables(self.model.variables())
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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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if __name__ == "__main__":
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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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ray.init()
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ModelCatalog.register_custom_model("my_model", CustomModel)
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tune.run(
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"PPO",
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stop={
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"timesteps_total": 10000,
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},
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config={
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"env": SimpleCorridor, # or "corridor" if registered above
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"model": {
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"custom_model": "my_model",
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},
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"vf_share_layers": True,
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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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"env_config": {
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"corridor_length": 5,
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},
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},
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)
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