103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
# %%
|
|
import gym
|
|
from options.options import gail_ppo, Buffer
|
|
from core.value import SetValue
|
|
from core.policy import SetDiscretePolicy
|
|
from core.discriminator import DeepsetDiscriminator
|
|
import torch.optim
|
|
from intersim.envs import IntersimpleLidarFlatRandom
|
|
from intersim.envs.intersimple import speed_reward
|
|
import functools
|
|
from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
|
|
import numpy as np
|
|
from options.options import OptionsEnv
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
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 = [OptionsEnv(Setobs(
|
|
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))
|
|
), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]
|
|
|
|
env_fn = lambda i: envs[i]
|
|
|
|
policy = SetDiscretePolicy(env_fn(0).action_space.n)
|
|
pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
|
|
|
|
value = SetValue()
|
|
v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
|
|
|
|
discriminator = DeepsetDiscriminator()
|
|
disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
|
|
|
|
expert_data = torch.load('intersimple-expert-data-setobs2.pt')
|
|
expert_data = Buffer(*expert_data)
|
|
|
|
# %%
|
|
def callback(epoch, value, policy):
|
|
if not epoch % 10:
|
|
torch.save(policy.state_dict(), f'gail-ppo-options-setobs2-{epoch}.pt')
|
|
torch.save(value.state_dict(), f'gail-ppo-options-setobs2-value-{epoch}.pt')
|
|
|
|
value, policy = gail_ppo(
|
|
env_fn=env_fn,
|
|
expert_data=expert_data,
|
|
discriminator=discriminator,
|
|
disc_opt=disc_opt,
|
|
disc_iters=100,
|
|
policy=policy,
|
|
value=value,
|
|
v_opt=v_opt,
|
|
v_iters=1000,
|
|
epochs=200,
|
|
rollout_episodes=60,
|
|
rollout_steps=60,
|
|
gamma=0.99,
|
|
gae_lambda=0.9,
|
|
clip_ratio=0.2,
|
|
pi_opt=pi_opt,
|
|
pi_iters=100,
|
|
logger=SummaryWriter(comment='gail-ppo-options-setobs2'),
|
|
callback=callback,
|
|
)
|
|
|
|
torch.save(policy.state_dict(), 'gail-ppo-options-setobs2.pt')
|
|
|
|
# %%
|
|
policy = SetDiscretePolicy(env_fn(0).action_space.n)
|
|
policy(torch.zeros(env_fn(0).observation_space.shape))
|
|
policy.load_state_dict(torch.load('gail-ppo-options-setobs2.pt'))
|
|
|
|
env = env_fn(0)
|
|
obs = env.reset()
|
|
env.render(mode='post')
|
|
for i in range(300):
|
|
action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
|
|
obs, reward, done, _ = env.step(action, render_mode='post')
|
|
print('step', i, 'reward', reward)
|
|
if done:
|
|
break
|
|
env.close() |