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33 lines
967 B
33 lines
967 B
import ray.rllib.agents.ppo as ppo
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from ray.tune.logger import pretty_print
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import os
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import ray
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'''
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The following switch allows the program to run locally or on the Agit distributed cluster without modifications.
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'''
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if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
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from agit import ray_init
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ray_init()
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else:
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ray.init()
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config = ppo.DEFAULT_CONFIG.copy()
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config["num_gpus"] = 0
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config["num_workers"] = 1
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config["eager"] = False
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# Create a trainer that holds PPO policy for environment interaction.
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trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
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checkpoint_path = ""
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# Can optionally call trainer.restore(checkpoint_path) to load a checkpoint.
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for i in range(1000):
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# Perform one iteration of training the policy with PPO
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result = trainer.train()
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print(pretty_print(result))
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if i % 100 == 0:
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checkpoint = trainer.save(checkpoint_path)
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print("checkpoint saved at", checkpoint)
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