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