101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
# %%
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import gym
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from options.options import gail
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from core.gail import Buffer
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from core.value import SetValue
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from core.policy import SetDiscretePolicy
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from core.discriminator import DeepsetDiscriminator
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import torch.optim
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from intersim.envs import IntersimpleLidarFlatRandom
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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, TransformObservation, Setobs
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import numpy as np
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from options.options import OptionsEnv
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from torch.utils.tensorboard import SummaryWriter
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from core.reparam_module import ReparamPolicy
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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 = [OptionsEnv(Setobs(
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TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
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n_rays=5,
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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))
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), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]
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env_fn = lambda i: envs[i]
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policy = SetDiscretePolicy(env_fn(0).action_space.n)
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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, weight_decay=1e-3)
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expert_data = torch.load('intersimple-expert-data-setobs2.pt')
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expert_data = Buffer(*expert_data)
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# %%
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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=100,
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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=200,
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rollout_episodes=60,
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rollout_steps=60,
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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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wasserstein=True,
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wasserstein_c=1.,
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logger=SummaryWriter(comment='wgail-options-setobs2'),
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)
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torch.save(policy.state_dict(), 'wgail-options-setobs2.pt')
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# %%
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policy = SetDiscretePolicy(env_fn(0).action_space.n)
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policy(torch.zeros(env_fn(0).observation_space.shape))
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policy = ReparamPolicy(policy)
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policy.load_state_dict(torch.load('wgail-options-setobs2.pt'))
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env = env_fn(0)
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obs = env.reset()
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env.render(mode='post')
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for i in range(300):
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#action, _ = policy.predict(torch.tensor(obs))
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action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
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obs, reward, done, _ = env.step(action, render_mode='post')
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print('step', i, 'reward', reward)
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if done:
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break
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env.close()
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