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446cbdf2e0
* Add `RandomEnv` example to examples folder. Convert warning into Error message when using an LSTM in a non-shared-vf network (after the warning, the program would crash). * LINT. * Fix issue #6884. LSTM + non-shared vf NN + PPO crashes when using a Tuple action space. * LINT * Change warning message for Model: shared_vf=False, LSTM=True cases. * Bug fix. * Add examples/random_env.py test to Jenkins.
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
"""
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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 gym
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from gym.spaces import Tuple, Discrete
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import numpy as np
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from ray.rllib.agents.ppo import PPOTrainer
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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class RandomEnv(gym.Env):
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"""
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A randomly acting environment that can be instantiated with arbitrary
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action and observation spaces.
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"""
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def __init__(self, config):
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# Action space.
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self.action_space = config["action_space"]
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# Observation space from which to sample.
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self.observation_space = config["observation_space"]
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# Reward space from which to sample.
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self.reward_space = config.get(
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"reward_space",
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gym.spaces.Box(low=-1.0, high=1.0, shape=(), dtype=np.float32))
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# Chance that an episode ends at any step.
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self.p_done = config.get("p_done", 0.1)
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def reset(self):
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return self.observation_space.sample()
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def step(self, action):
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return self.observation_space.sample(), \
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float(self.reward_space.sample()), \
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bool(np.random.choice(
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[True, False], p=[self.p_done, 1.0 - self.p_done]
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)), {}
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if __name__ == "__main__":
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trainer = PPOTrainer(
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config={
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"model": {
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"use_lstm": True,
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},
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"vf_share_layers": False,
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"num_workers": 0, # no parallelism
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"env_config": {
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"action_space": Discrete(2),
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# Test a simple Tuple observation space.
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"observation_space": Tuple([Discrete(3),
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Discrete(2)])
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}
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},
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env=RandomEnv,
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)
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results = trainer.train()
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print(results)
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