Datasets:
jblitzar
/
github-python

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state = env.reset()
episode_reward = 0.
done = False
while not done:
if render:
env.render()
time.sleep(0.01)
action = agent.sample_action(state, eval=True)
next_state, reward, done, _ = env.step(action)
episode_reward += reward
state = next_state
print(episode_reward)
returns[i] = episode_reward
mean_return = np.mean(returns)
std_return = np.std(returns)
print('-' * 60)
print(f'Num steps: {steps:<5} '
f'reward: {mean_return:<5.1f} '
f'std: {std_return:<5.1f}')
print(returns)
print('-' * 60)
return mean_return
def main(args=None):
device = torch.device(args.cuda)
dir = "record"
# dir = "test"
log_dir = os.path.join(dir, f'{args.env_name}', f'policy_type={args.policy_type}', f'ratio={args.ratio}',
f'seed={args.seed}')
# Initial environment
env = gym.make(args.env_name)
eval_env = copy.deepcopy((env))
state_size = int(np.prod(env.observation_space.shape))
action_size = int(np.prod(env.action_space.shape))
print(action_size)
# Set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
env.seed(args.seed)
eval_env.seed(args.seed)
memory_size = 1e6
num_steps = args.num_steps
start_steps = 10000
eval_interval = 10000
updates_per_step = 1
batch_size = args.batch_size
log_interval = 10
memory = ReplayMemory(state_size, action_size, memory_size, device)
diffusion_memory = DiffusionMemory(state_size, action_size, memory_size, device)
agent = QVPO(args, state_size, env.action_space, memory, diffusion_memory, device)
agent.load_model(os.path.join('./results', prefix + '_' + name), id=args.id)
if os.path.exists(os.path.join('./results', prefix + '_' + name, 'config_' + args.id[:-2] + '.pkl')):
with open(os.path.join('./results', prefix + '_' + name, 'config_' + args.id[:-2] + '.pkl'), 'rb') as f:
conf = pickle.load(f)
for k, v in conf._get_kwargs():
print(f"{k}: {v}")
steps = 0
episodes = 0
best_result = 0
if steps % eval_interval == 0:
evaluate(eval_env, agent, steps, args.render)
if __name__ == "__main__":
args = readParser()
if args.target_sample == -1:
args.target_sample = args.behavior_sample
## settings
prefix = 'qvpo'
name = args.env_name
keys = ("epoch", "reward")
times = args.times
## run
for t in range(times):
main(args)
# <FILESEP>
import json
import requests
import time
import datetime
from collections import defaultdict
class Webhook:
def __init__(self, url, **kwargs):