def fn():
env = gym.make(env_id)
if 'SuperMarioBros' in env_id:
env = ReshapeReward(env, monitor=None)
env = SkipObs(env)
env = SingleEnv(env)
env.seed(seed + rank)
env.action_space.seed(seed + rank)
return env
def eval_policy(self, env_name, seed, eval_episodes=5):
'''Runs policy for X episodes and returns average reward
A fixed seed is used for the eval environment
Only off-policy algos need this'''
eval_env = gym.make(env_name)
eval_env = SingleEnv(eval_env)
eval_env.seed(seed + 100)
# Assert env is wrapped by TimeLimit and env has attr _max_episode_steps
for _ in range(eval_episodes):
obs, done, ep_rew, ep_len = eval_env.reset(), False, 0., 0
while not done:
# Take deterministic actions at test time (noise=0)
obs, rew, done, _ = eval_env.step(self.select_action(obs))
ep_rew += rew
ep_len += 1
self.logger.store(TestEpRet=ep_rew, TestEplen=ep_len)
parser.add_argument('--policy', type=str, default='mp_ppo')
parser.add_argument('--env', type=str, default='SuperMarioBros-8-1-v0')
parser.add_argument('--policy_type', type=str, default='cnn')
args = parser.parse_args()
exp_name = args.policy + '_' + args.env
output = f'./data/{exp_name}/{exp_name}_seed_0'
env = gym.make(args.env)
if 'SuperMario' in args.env:
env = JoypadSpace(env, SIMPLE_MOVEMENT)
env = ReshapeReward(env, monitor=None)
env = SkipObs(env)
env = SingleEnv(env)
env = gym.wrappers.Monitor(env,
DEFAULT_VIDEO_DIR,
force=True,
video_callable=lambda episode_id: True)
model_name = os.path.join(output, 'pytorch_save', 'model.pth')
save_name = os.path.join(output, 'environment_vars.pkl')
kwargs = {
'env':
env,
"actor_critic":
core.CNNActorCritic
if args.policy_type == 'cnn' else core.MLPActorCritic,
'ac_kwargs':
dict(hidden_sizes=[64] * 2),
if __name__ == '__main__' and '__file__' in globals():
parser = argparse.ArgumentParser()
parser.add_argument('--policy_name', type=str, default='td3')
parser.add_argument('--env_name', type=str, default='HalfCheetahPyBulletEnv-v0')
parser.add_argument('--policy_type', type=str, default='mlp')
parser.add_argument('--seed', type=int, default=0)
args = parser.parse_args()
exp_name = args.policy_name + '_' + args.env_name
output=f'./data/F_{exp_name}/{exp_name}_seed_{args.seed}'
env = gym.make(args.env_name)
env = SingleEnv(env)
env = gym.wrappers.Monitor(env, DEFAULT_VIDEO_DIR, force=True, video_callable=lambda episode_id: True)
model_name = os.path.join(output, 'pytorch_save', 'model.pth')
save_name = os.path.join(output, 'environment_vars.pkl')
kwargs = {
'env': env,
"actor_critic": core.MLPDetActorCritic,
'ac_kwargs': dict(hidden_sizes=[400,300]),
'device' : 'cpu'
}
policy = TD3(**kwargs)
policy.ac.load_state_dict(torch.load(model_name, map_location='cpu'))
# This Command for slower(setpts > 1.0) of faster(setpts < 1.0) video