45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
import gym
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from core.gail import gail_ppo, Buffer
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from core.value import Value
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from core.policy import Policy
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from core.discriminator import Discriminator
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import torch.optim
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env_fn = lambda _: gym.make('Pendulum-v0')
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policy = Policy(env_fn(0).action_space.shape[0])
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pi_opt = torch.optim.RMSprop(policy.parameters(), lr=3e-4)
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value = Value()
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v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
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discriminator = Discriminator()
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disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-3)
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expert_data = torch.load('trpo-pendulum-expert-data.pt')
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expert_data = Buffer(*expert_data)
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gail_ppo(
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env_fn=env_fn,
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expert_data=expert_data,
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discriminator=discriminator,
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disc_opt=disc_opt,
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disc_iters=10,
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policy=policy,
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value=value,
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v_opt=v_opt,
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v_iters=1000,
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epochs=100,
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rollout_episodes=20,
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rollout_steps=200,
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gamma=0.99,
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gae_lambda=0.9,
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clip_ratio=0.2,
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pi_opt=pi_opt,
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pi_iters=100,
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wasserstein=True,
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wasserstein_c=100.,
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)
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torch.save(policy.state_dict(), 'gail-pendulum.pt')
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