def experiment(args):
device = torch.device(
"cuda:{}".format(args.device) if args.cuda else "cpu")
env = get_vec_env(params["env_name"], params["env"], args.vec_env_nums)
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
buffer_param = params['replay_buffer']
experiment_name = os.path.split(
os.path.splitext(args.config)[0])[-1] if args.id is None \
else args.id
logger = Logger(experiment_name, params['env_name'], args.seed, params,
args.log_dir, args.overwrite)
params['general_setting']['env'] = env
replay_buffer = OnPolicyReplayBuffer(
env_nums=args.vec_env_nums,
max_replay_buffer_size=int(buffer_param['size']),
time_limit_filter=buffer_param['time_limit_filter'])
params['general_setting']['replay_buffer'] = replay_buffer
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type'] = networks.MLPBase
params['net']['activation_func'] = torch.nn.Tanh
pf = policies.GuassianContPolicyBasicBias(
input_shape=env.observation_space.shape[0],
output_shape=env.action_space.shape[0],
**params['net'],
**params['policy'])
vf = networks.Net(input_shape=env.observation_space.shape,
output_shape=1,
**params['net'])
print(pf)
print(vf)
params['general_setting']['collector'] = VecOnPolicyCollector(
vf,
env=env,
pf=pf,
replay_buffer=replay_buffer,
device=device,
train_render=False,
**params["collector"])
params['general_setting']['save_dir'] = osp.join(logger.work_dir, "model")
agent = A2C(pf=pf, vf=vf, **params["a2c"], **params["general_setting"])
agent.train()
def experiment(args):
# import torch.multiprocessing as mp
# mp.set_start_method('spawn')
device = torch.device(
"cuda:{}".format(args.device) if args.cuda else "cpu")
env = get_env(params['env_name'], params['env'])
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
buffer_param = params['replay_buffer']
experiment_name = os.path.split(os.path.splitext(args.config)[0])[-1] if args.id is None \
else args.id
logger = Logger(experiment_name, params['env_name'], args.seed, params,
args.log_dir)
params['general_setting']['env'] = env
replay_buffer = OnPolicyReplayBuffer(
int(buffer_param['size']),
time_limit_filter=buffer_param['time_limit_filter'])
params['general_setting']['replay_buffer'] = replay_buffer
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type'] = networks.MLPBase
params['net']['activation_func'] = nn.Tanh
pf = policies.GuassianContPolicyBasicBias(
input_shape=env.observation_space.shape[0],
output_shape=env.action_space.shape[0],
init_func=lambda x: init.orthogonal_init(
x, scale=np.sqrt(2), constant=0),
net_last_init_func=lambda x: init.orthogonal_init(
x, scale=0.01, constant=0),
**params['net'],
**params['policy'])
vf = networks.Net(input_shape=env.observation_space.shape,
output_shape=1,
init_func=lambda x: init.orthogonal_init(
x, scale=np.sqrt(2), constant=0),
net_last_init_func=lambda x: init.orthogonal_init(
x, scale=1, constant=0),
**params['net'])
params['general_setting']['collector'] = OnPlicyCollectorBase(
vf,
env=env,
pf=pf,
replay_buffer=replay_buffer,
device=device,
train_render=False,
**params["collector"])
params['general_setting']['save_dir'] = osp.join(logger.work_dir, "model")
agent = TRPO(pf=pf, vf=vf, **params["trpo"], **params["general_setting"])
print(params["general_setting"])
print(agent.epoch_frames)
agent.train()