import gym from core.gail import gail, Buffer from core.value import Value from core.policy import Policy from core.discriminator import Discriminator import torch.optim from intersim.envs import IntersimpleLidarFlat from intersim.envs.intersimple import speed_reward import functools from wrappers import CollisionPenaltyWrapper import numpy as np from gym.wrappers import TransformObservation obs_min = np.array([ [-1000, -1000, 0, -np.pi, -1e-1, 0.], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], ]).reshape(-1) obs_max = np.array([ [1000, 1000, 20, np.pi, 1e-1, 0.], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], ]).reshape(-1) envs = [TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat( n_rays=5, agent=51, reward=functools.partial( speed_reward, collision_penalty=0 ), stop_on_collision=False, ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(30)] env_fn = lambda i: envs[i] policy = Policy(env_fn(0).action_space.shape[0]) value = Value() v_opt = torch.optim.Adam(value.parameters(), lr=1e-4) discriminator = Discriminator() disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-4) expert_data = torch.load('intersimple-expert-data-normobs.pt') expert_data = Buffer(*expert_data) value, policy = gail( env_fn=env_fn, expert_data=expert_data, discriminator=discriminator, disc_opt=disc_opt, disc_iters=10, policy=policy, value=value, v_opt=v_opt, v_iters=1000, epochs=4000, rollout_episodes=30, rollout_steps=100, gamma=0.99, gae_lambda=0.9, delta=0.01, backtrack_coeff=0.8, backtrack_iters=10, ) torch.save(policy.state_dict(), 'gail-intersimple-normobs.pt')