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58 lines
1.9 KiB
Python
Executable file
58 lines
1.9 KiB
Python
Executable file
#!/usr/bin/env python
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"""Example of training with a policy server. Copy this file for your use case.
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To try this out, in two separate shells run:
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$ python cartpole_server.py --run=[PPO|DQN]
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$ python cartpole_client.py --inference-mode=local|remote
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Local inference mode offloads inference to the client for better performance.
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"""
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import argparse
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import gym
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from ray.rllib.env.policy_client import PolicyClient
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--no-train", action="store_true", help="Whether to disable training.")
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parser.add_argument(
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"--inference-mode", type=str, default="local", choices=["local", "remote"])
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parser.add_argument(
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"--off-policy",
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action="store_true",
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help="Whether to take random instead of on-policy actions.")
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parser.add_argument(
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"--stop-reward",
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type=int,
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default=9999,
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help="Stop once the specified reward is reached.")
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if __name__ == "__main__":
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args = parser.parse_args()
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env = gym.make("CartPole-v0")
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client = PolicyClient(
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"http://localhost:9900", inference_mode=args.inference_mode)
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eid = client.start_episode(training_enabled=not args.no_train)
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obs = env.reset()
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rewards = 0
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while True:
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if args.off_policy:
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action = env.action_space.sample()
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client.log_action(eid, obs, action)
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else:
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action = client.get_action(eid, obs)
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obs, reward, done, info = env.step(action)
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rewards += reward
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client.log_returns(eid, reward, info=info)
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if done:
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print("Total reward:", rewards)
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if rewards >= args.stop_reward:
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print("Target reward achieved, exiting")
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exit(0)
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rewards = 0
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client.end_episode(eid, obs)
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obs = env.reset()
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eid = client.start_episode(training_enabled=not args.no_train)
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