metavar='N',
help='model updates per simulator step (default: 5)')
parser.add_argument('--num-stack',
type=int,
default=1,
help='number of frames to stack')
parser.add_argument('--model-suffix',
default="",
help='To resume training or not')
args = parser.parse_args()
env = NormalizedActions(gym.make(args.env_name))
writer = SummaryWriter()
env.seed(args.seed)
if torch.cuda.is_available():
device = torch.device("cuda:0")
torch.cuda.manual_seed(args.seed)
else:
device = torch.device("cpu")
torch.manual_seed(args.seed)
np.random.seed(args.seed)
obs_shape = env.observation_space.shape
obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
if len(env.observation_space.shape) == 3:
image_input = True
json.dump(vars(args), outfile)
cnn = args.pics
for i_run in range(args.max_num_run):
logger.important(f"START TRAINING RUN {i_run}")
# Make the environment
env = gym.make(args.env_name)
env._max_episode_steps = args.max_num_step
env = NormalizedActions(env)
if cnn:
env = ImageWrapper(args.img_size, env)
# Set Seed for repeatability
torch.manual_seed(args.seed + i_run)
np.random.seed(args.seed + i_run)
env.seed(args.seed + i_run)
env.action_space.np_random.seed(args.seed + i_run)
# Setup the agent
agent = SAC(args.state_buffer_size, env.action_space, args)
# Setup TensorboardX
writer_train = SummaryWriter(log_dir='runs/' + folder + 'run_' +
str(i_run) + '/train')
writer_test = SummaryWriter(log_dir='runs/' + folder + 'run_' +
str(i_run) + '/test')
# Setup Replay Memory
memory = ReplayMemory(args.replay_size)
# TRAINING LOOP
help='max episode length (default: 1000)')
parser.add_argument('--num_episodes', type=int, default=1000, metavar='N',
help='number of episodes (default: 1000)')
parser.add_argument('--hidden_size', type=int, default=128, metavar='N',
help='number of episodes (default: 128)')
parser.add_argument('--updates_per_step', type=int, default=5, metavar='N',
help='model updates per simulator step (default: 5)')
parser.add_argument('--replay_size', type=int, default=1000000, metavar='N',
help='size of replay buffer (default: 1000000)')
args = parser.parse_args()
env = NormalizedActions(gym.make(args.env_name))
writer = SummaryWriter()
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if args.algo == "NAF":
agent = NAF(args.gamma, args.tau, args.hidden_size,
env.observation_space.shape[0], env.action_space)
else:
agent = DDPG(args.gamma, args.tau, args.hidden_size,
env.observation_space.shape[0], env.action_space)
memory = ReplayMemory(args.replay_size)
ounoise = OUNoise(env.action_space.shape[0]) if args.ou_noise else None
param_noise = AdaptiveParamNoiseSpec(initial_stddev=0.05,
desired_action_stddev=args.noise_scale, adaptation_coefficient=1.05) if args.param_noise else None
env = sys.argv[1]
args = None
if env == 'mc':
args = args_mc
elif env == 'pd':
args = args_pd
elif env == 'll':
args = args_ll
else:
print('Environment not selected, Please choose from: mc, pd,ll')
exit(-1)
env = NormalizedActions(gym.make(args['env_name']))
env.seed(args['seed'])
torch.manual_seed(args['seed'])
np.random.seed(args['seed'])
agent = NAF(args['gamma'], args['tau'], args['hidden_size'],
env.observation_space.shape[0], env.action_space)
agent.load_model(f'models/naf_{args["env_name"]}')
replay_buffer = ReplayBuffer(args['replay_size'])
ounoise = OUNoise(env.action_space.shape[0]) if args['ou_noise'] else None
run()
plot_results()