75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
import gym
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from core.gail import gail, Buffer
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from core.value import SetValue
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from core.policy import SetPolicy
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from core.discriminator import DeepsetDiscriminator
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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 wrappers import CollisionPenaltyWrapper, Setobs
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import numpy as np
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from gym.wrappers import TransformObservation
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obs_min = np.array([
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[-1000, -1000, 0, -np.pi, -1e-1, 0.],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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]).reshape(-1)
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obs_max = np.array([
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[1000, 1000, 20, np.pi, 1e-1, 0.],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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]).reshape(-1)
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envs = [Setobs(TransformObservation(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), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
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env_fn = lambda i: envs[i]
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policy = SetPolicy(env_fn(0).action_space.shape[0])
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value = SetValue()
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v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
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discriminator = DeepsetDiscriminator()
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disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
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expert_data = torch.load('intersimple-expert-data-setobs.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=500,
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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=200,
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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-setobs.pt')
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