args = get_ddpg_args_test() experiment = args.experiment seed = args.seed policy_name = "DDPG" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") file_name = "{}_{}_{}".format(policy_name, experiment, seed) # Launch the env with our helper function env = launch_env() # Wrappers env = ResizeWrapper(env) env = NormalizeWrapper(env) env = ImgWrapper(env) # to make the images from 160x120x3 into 3x160x120 env = ActionWrapper(env) # env = DtRewardWrapper(env) # not during testing state_dim = env.observation_space.shape action_dim = env.action_space.shape[0] max_action = float(env.action_space.high[0]) # Initialize policy policy = DDPG(state_dim, action_dim, max_action, net_type="cnn") policy.load(file_name, directory="./pytorch_models") with torch.no_grad():
file_name = "{}_{}_{}".format( policy_name, experiment, str(args.seed), ) if not os.path.exists("./results"): os.makedirs("./results") if args.save_models and not os.path.exists("./pytorch_models"): os.makedirs("./pytorch_models") # Launch the env with our helper function env = launch_env() # Wrappers env = ResizeWrapper(env) env = NormalizeWrapper(env) env = ImgWrapper(env) # to make the images from 160x120x3 into 3x160x120 env = ActionWrapper(env) env = DtRewardWrapper(env) # Set seeds seed(args.seed) state_dim = env.observation_space.shape action_dim = env.action_space.shape[0] max_action = float(env.action_space.high[0]) # Initialize policy policy = DDPG(state_dim, action_dim, max_action, net_type="cnn")