raise ValueError(f"Invalid Policy: {args.policy}!")
if args.save_model and not os.path.exists("./models"):
os.makedirs("./models")
if args.load_model != "":
policy_file = file_name if args.load_model == "default" else args.load_model
if not os.path.exists(f"./models/{policy_file}"):
assert f"The loading model path of `../models/{policy_file}` does not exist! "
policy.load(f"./models/{policy_file}")
# Setup loggers
logger_kwargs = setup_logger_kwargs(args.exp_name,
args.seed,
datestamp=False)
logger = EpochLogger(**logger_kwargs)
# Sync params across processes
sync_params(policy)
# Set up experience buffer
local_steps_per_epoch = int(args.steps_per_epoch / num_procs())
_replay_buffer = replay_buffer.VPGBuffer(state_dim, action_dim,
local_steps_per_epoch,
args.discount, args.lam,
is_discrete)
state, done = env.reset(), False
episode_reward = 0
episode_timesteps = 0
episode_num = 0
raise ValueError(f"Invalid Policy: {args.policy}!")
if args.save_model and not os.path.exists("./models"):
os.makedirs("./models")
if args.load_model != "":
policy_file = file_name if args.load_model == "default" else args.load_model
if not os.path.exists(f"./models/{policy_file}"):
assert f"The loading model path of `../models/{policy_file}` does not exist! "
policy.load(f"./models/{policy_file}")
# Setup loggers
logger_kwargs = setup_logger_kwargs(args.exp_name,
args.seed,
datestamp=False)
logger = EpochLogger(**logger_kwargs)
_replay_buffer = replay_buffer.ReplayBuffer(int(args.buffer_size))
print("Collecting experience...")
epinfobuf = deque(maxlen=100) # episode step for accumulate reward
start_time = time.time() # check learning time
states = np.array(
env.reset()) # env reset, output array of num of `#num_envs` states
step = 0
for t in range(1, int(args.max_timesteps) // int(args.num_envs) + 1):
actions = policy.select_action(states, eps_schedule.value)
next_states, rewards, dones, infos = env.step(
actions) # take actions and get next states