break
env_t.close()
env_name = 'CartPole-v1'
gamma = 0.99
num_envs = 8
PENALTY = -1.0
n_step = 4
max_frame = 50000
lr = 0.001
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if __name__ == '__main__':
envs = SubprocVecEnv([make_env(env_name) for i in range(num_envs)])
net = nn.Sequential(nn.Linear(4, 128), nn.ReLU(), nn.Linear(128, 2))
actor = Actor(4, 128, 2).to(device)
critic = Critic(4, 128).to(device)
solver = optim.Adam(
list(actor.parameters()) + list(critic.parameters()), lr)
duration = []
frame_count = 0
lifespan = [[0] for _ in range(num_envs)]
s_gotten = None
while frame_count * n_step < max_frame:
obs_l, acts_l, rews_l, dones_l, probs_l = [], [], [], [], []
accept_sample = [True for _ in range(num_envs)]
for _ in range(n_step):
from common.multiprocessing_env import SubprocVecEnv, make_env
from ddpg import *
from utils import *
from memory import Memory
from ddpg_pendulum_net import Actor, Critic
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
env_name = 'Pendulum-v0'
num_envs = 8
envs = [make_env(env_name) for i in range(num_envs)]
envs = SubprocVecEnv(envs)
actor_lr = 2e-4
critic_lr = 3e-4
gamma = 0.99
soft_tau = 0.001
model = DDPG(Actor(), Critic(), gamma, soft_tau, actor_lr, critic_lr)
memory_size = 500000
memory = Memory(memory_size)
eval_freq = 1000
batch_size = 256
n_steps = 1
num_ddpg_iter = 5
max_iteration = 50000
frame_count = 0