63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import gym
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from core.sampling import rollout
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from core.trpo import trpo
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from core.value import Value
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from core.policy import Policy
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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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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 = [TransformObservation(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=10
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),
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), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(50)]
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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, weight_decay=1e-3)
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value, policy = trpo(
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env_fn=env_fn,
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value=value,
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policy=policy,
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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=0.95,
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delta=0.01,
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backtrack_coeff=0.9,
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backtrack_iters=50,
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v_opt=v_opt,
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v_iters=1000,
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cg_damping=0.1,
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)
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#rollout(env_fn, policy, n_episodes=9, max_steps_per_episode=200, render=True)
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