55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
import gym
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from core.gail import gail, 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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from intersim.envs import IntersimpleLidarFlat
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from intersim.envs.intersimple import speed_reward
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import functools
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from util.wrappers import CollisionPenaltyWrapper
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envs = [CollisionPenaltyWrapper(IntersimpleLidarFlat(
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n_rays=5,
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agent=51,
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reward=functools.partial(
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speed_reward,
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collision_penalty=0
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),
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stop_on_collision=False,
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), collision_distance=6, collision_penalty=100) for _ in range(30)]
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env_fn = lambda i: envs[i]
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policy = Policy(env_fn(0).action_space.shape[0])
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value = Value()
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v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
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discriminator = Discriminator()
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disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-4)
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expert_data = torch.load('intersimple-expert-data.pt')
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expert_data = Buffer(*expert_data)
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value, policy = gail(
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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=4000,
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rollout_episodes=30,
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rollout_steps=100,
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gamma=0.99,
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gae_lambda=0.9,
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delta=0.01,
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backtrack_coeff=0.8,
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backtrack_iters=10,
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)
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torch.save(policy.state_dict(), 'gail-intersimple.pt')
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