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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.
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import numpy as np
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from ray.rllib.agents.trainer import Trainer, with_common_config
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from ray.rllib.utils.annotations import override
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# yapf: disable
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# __sphinx_doc_begin__
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class RandomAgent(Trainer):
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"""Policy that takes random actions and never learns."""
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_name = "RandomAgent"
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_default_config = with_common_config({
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"rollouts_per_iteration": 10,
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})
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@override(Trainer)
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def _init(self, config, env_creator):
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self.env = env_creator(config["env_config"])
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@override(Trainer)
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def _train(self):
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rewards = []
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steps = 0
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for _ in range(self.config["rollouts_per_iteration"]):
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obs = self.env.reset()
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done = False
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reward = 0.0
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while not done:
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action = self.env.action_space.sample()
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obs, r, done, info = self.env.step(action)
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reward += r
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steps += 1
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rewards.append(reward)
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return {
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"episode_reward_mean": np.mean(rewards),
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"timesteps_this_iter": steps,
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}
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# __sphinx_doc_end__
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# don't enable yapf after, it's buggy here
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if __name__ == "__main__":
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trainer = RandomAgent(
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env="CartPole-v0", config={"rollouts_per_iteration": 10})
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result = trainer.train()
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assert result["episode_reward_mean"] > 10, result
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print("Test: OK")
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