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InteractionImitation/scratch/etienne/trpo/experiments/wgail-intersimple-minobs2.py
2022-02-17 22:41:55 +01:00

76 lines
2.1 KiB
Python

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 IntersimpleLidarFlatRandom
from intersim.envs.intersimple import speed_reward
import functools
from util.wrappers import CollisionPenaltyWrapper, Minobs
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 = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
n_rays=5,
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(50)]
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, weight_decay=1e-3)
discriminator = Discriminator()
disc_opt = torch.optim.RMSprop(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
expert_data = torch.load('intersimple-expert-data-minobs2.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=500,
policy=policy,
value=value,
v_opt=v_opt,
v_iters=1000,
epochs=4000,
rollout_episodes=50,
rollout_steps=200,
gamma=0.99,
gae_lambda=0.9,
delta=0.01,
backtrack_coeff=0.8,
backtrack_iters=10,
wasserstein=True,
wasserstein_c=0.1,
)
torch.save(policy.state_dict(), 'wgail-intersimple-minobs2.pt')