31 lines
718 B
Python
31 lines
718 B
Python
import gym
|
|
from gym.wrappers import TransformObservation
|
|
from core.trpo import trpo
|
|
from core.value import Value
|
|
from core.policy import Policy
|
|
import torch.optim
|
|
|
|
env_fn = lambda _: TransformObservation(gym.make('Pendulum-v0'), lambda obs: obs)
|
|
policy = Policy(env_fn(0).action_space.shape[0])
|
|
value = Value()
|
|
v_opt = torch.optim.Adam(value.parameters(), lr=1e-3, weight_decay=1e-4)
|
|
|
|
value, policy = trpo(
|
|
env_fn=env_fn,
|
|
value=value,
|
|
policy=policy,
|
|
epochs=100,
|
|
rollout_episodes=20,
|
|
rollout_steps=250,
|
|
gamma=0.99,
|
|
gae_lambda=0.9,
|
|
delta=0.01,
|
|
backtrack_coeff=0.8,
|
|
backtrack_iters=10,
|
|
v_opt=v_opt,
|
|
v_iters=1000,
|
|
)
|
|
|
|
|
|
torch.save(policy.state_dict(), 'trpo-pendulum.pt')
|