if args.save_azure_container is not None:
account_name, account_key, container_name = \
args.save_azure_container.split(":")
container = Container(account_name=account_name,
account_key=account_key,
container_name=container_name,
maybe_create=True)
if savedir is None:
# Careful! This will not get cleaned up.
savedir = tempfile.TemporaryDirectory().name
else:
container = None
# Create and seed the env.
env, monitored_env = make_env(args.env)
if args.seed > 0:
set_global_seeds(args.seed)
env.unwrapped.seed(args.seed)
# V: Save arguments, configure log dump path to savedir #
if savedir:
with open(os.path.join(savedir, 'args.json'), 'w') as f:
json.dump(vars(args), f)
logger.configure(dir=savedir) # log to savedir
with U.make_session(4) as sess:
# Create training graph and replay buffer
act, train, update_target, debug, craft_adv = deepq.build_train(
make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape,
name=name),
q_func=dueling_model if args.dueling else model,
num_actions=env.action_space.n,
args.save_azure_container.split(":")
container = Container(
account_name=account_name,
account_key=account_key,
container_name=container_name,
maybe_create=True
)
if savedir is None:
# Careful! This will not get cleaned up.
savedir = tempfile.TemporaryDirectory().name
else:
container = None
# Create and seed the env.
env, monitored_env = make_env(args.env)
if args.seed > 0:
set_global_seeds(args.seed)
env.unwrapped.seed(args.seed)
# V: Save arguments, configure log dump path to savedir #
if savedir:
with open(os.path.join(savedir, 'args.json'), 'w') as f:
json.dump(vars(args), f)
logger.configure(dir=savedir) # log to savedir
with U.make_session(4) as sess:
# Create training graph and replay buffer
act, train, update_target, debug, craft_adv = deepq.build_train(
make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape,
name=name),
q_func=dueling_model if args.dueling else model,
num_actions=env.action_space.n,