def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# create the network
self.actor_network = actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
self.actor_target_network = actor(env_params)
self.critic_target_network = critic(env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(self.actor_network.state_dict())
self.critic_target_network.load_state_dict(self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network.cuda()
self.critic_network.cuda()
self.actor_target_network.cuda()
self.critic_target_network.cuda()
# create the optimizer
if self.args.optimizer_type =='SGD':
self.actor_optim = torch.optim.SGD(self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.SGD(self.critic_network.parameters(), lr=self.args.lr_critic)
elif self.args.optimizer_type =='adam':
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(), lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy, self.args.replay_k, self.env.compute_reward)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size, self.her_module.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'], default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'], default_clip_range=self.args.clip_range)
self.scales = []
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir, self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
self.result_dir = f'./learning_curves/{args.env_name}/{self.args.run_name}'
if not os.path.isdir(self.result_dir):
os.makedirs(self.result_dir, exist_ok=True)
print(f'creating {self.result_dir}')
self.writer = SummaryWriter(logdir=self.result_dir)
def __init__(self, args, env, env_params):
self.args = args
# path to save the model
self.exp_name = '_'.join((self.args.env_name, self.args.alg,
str(self.args.seed), datetime.now().isoformat()))
self.data_path = os.path.join(self.args.save_dir,
'_'.join((self.args.env_name, self.args.alg)),
self.exp_name)
self.logger = EpochLogger(output_dir=self.data_path, exp_name=self.exp_name)
self.logger.save_config(args)
self.env = env
self.env_params = env_params
# create the network
self.actor_network = actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
self.actor_target_network = actor(env_params)
self.critic_target_network = critic(env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(self.actor_network.state_dict())
self.critic_target_network.load_state_dict(self.critic_network.state_dict())
# if use gpu
self.rank = MPI.COMM_WORLD.Get_rank()
if args.cuda:
device = 'cuda:{}'.format(self.rank % torch.cuda.device_count())
else:
device = 'cpu'
self.device = torch.device(device)
if self.args.cuda:
self.actor_network.cuda(self.device)
self.critic_network.cuda(self.device)
self.actor_target_network.cuda(self.device)
self.critic_target_network.cuda(self.device)
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(), lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy, self.args.replay_k, self.env.compute_reward)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size, self.her_module.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'], default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'], default_clip_range=self.args.clip_range)
self.logger.setup_pytorch_saver(self.actor_network)
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# create the network
self.actor_network = actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
self.actor_target_network = actor(env_params)
self.critic_target_network = critic(env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(
self.actor_network.state_dict())
self.critic_target_network.load_state_dict(
self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network.cuda()
self.critic_network.cuda()
self.actor_target_network.cuda()
self.critic_target_network.cuda()
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(),
lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(),
lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size,
self.her_module.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'],
default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# create the network
self.actor_network = actor(args, env_params)
self.critic_network = critic(args, env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
self.actor_target_network = actor(args, env_params)
self.critic_target_network = critic(args, env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(self.actor_network.state_dict())
self.critic_target_network.load_state_dict(self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network.cuda()
self.critic_network.cuda()
self.actor_target_network.cuda()
self.critic_target_network.cuda()
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(), lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy, self.args.replay_k, self.env.compute_reward)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size, self.her_module.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'], default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'], default_clip_range=self.args.clip_range)
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir, self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
# added
log_dir = create_env_folder(args.env_name, args.network_class, test=args.test)
save_kwargs(vars(args), log_dir)
tabular_log_path = osp.join(log_dir, 'progress.csv')
text_log_path = osp.join(log_dir, 'debug.log')
logger.add_text_output(text_log_path)
logger.add_tabular_output(tabular_log_path)
exp_name = f'{args.env_name}'
logger.push_prefix("[%s] " % exp_name)
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
sim = self.env.sim
self.viewer = MjRenderContextOffscreen(sim)
# self.viewer.cam.fixedcamid = 3
# self.viewer.cam.type = const.CAMERA_FIXED
self.critic_loss = []
self.actor_loss = []
self.viewer.cam.distance = 1.2
self.viewer.cam.azimuth = 180
self.viewer.cam.elevation = -25
env.env._viewers['rgb_array'] = self.viewer
self.env_params = env_params
self.image_based = True if args.image else False
print("Training image based RL ? : {}".format(self.image_based))
# create the network
if not self.image_based:
self.actor_network = actor(env_params)
else:
self.actor_network = new_actor(env_params)
#self.actor_network = resnet_actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
if not self.image_based:
self.actor_target_network = actor(env_params)
else:
#self.actor_target_network = resnet_actor(env_params)
self.actor_target_network = new_actor(env_params)
self.critic_target_network = critic(env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(
self.actor_network.state_dict())
self.critic_target_network.load_state_dict(
self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
print("use the GPU")
self.actor_network.cuda(MPI.COMM_WORLD.Get_rank())
self.critic_network.cuda(MPI.COMM_WORLD.Get_rank())
self.actor_target_network.cuda(MPI.COMM_WORLD.Get_rank())
self.critic_target_network.cuda(MPI.COMM_WORLD.Get_rank())
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(),
lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(),
lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward,
self.image_based)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size,
self.her_module.sample_her_transitions,
self.image_based)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'],
default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
def __init__(self, args, env1, env2, env1_params, env2_params):
self.args = args
self.env1 = env1
self.env2 = env2
self.env1_params = env1_params
self.env2_params = env2_params
if not self.args.dont_inject_observation:
self.env2_params['obs'] += 1
self.env1_params['obs'] += 1
self.train_mode = TrainMode(args.training_mode)
# store weights and biases API key if in args
if self.args.wandb_api_key is not None:
os.environ["WANDB_API_KEY"] = self.args.wandb_api_key
# if key is present set a flag to enable the functionality
self.use_wandb_log = os.environ.get("WANDB_API_KEY") is not None
# create the network
assert env1_params == env2_params # TODO: make sure to check for equality
self.actor_network = actor(env1_params)
self.critic_network = critic(env1_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
self.actor_target_network = actor(env1_params)
self.critic_target_network = critic(env1_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(self.actor_network.state_dict())
self.critic_target_network.load_state_dict(self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network.cuda()
self.critic_network.cuda()
self.actor_target_network.cuda()
self.critic_target_network.cuda()
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(), lr=self.args.lr_critic)
# setup dual critic if applicable
self.use_two_critics = self.args.dual_critic
if self.use_two_critics:
self.critic_network2 = critic(env1_params)
sync_networks(self.critic_network2)
self.critic_target_network2 = critic(env1_params)
self.critic_target_network2.load_state_dict(self.critic_network2.state_dict())
self.critic2_optim = torch.optim.Adam(self.critic_network2.parameters(), lr=self.args.lr_critic)
if self.args.cuda:
self.critic_network2.cuda()
self.critic_target_network2.cuda()
# her sampler
self.her_module1 = her_sampler(self.args.replay_strategy, self.args.replay_k, self.env1.compute_reward)
self.her_module2 = her_sampler(self.args.replay_strategy, self.args.replay_k, self.env2.compute_reward)
# create the replay buffer
self.buffer1 = replay_buffer(self.env1_params, self.args.buffer_size, self.her_module1.sample_her_transitions)
self.buffer2 = replay_buffer(self.env2_params, self.args.buffer_size, self.her_module2.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(size=env1_params['obs'], default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env1_params['goal'], default_clip_range=self.args.clip_range)
# create the dict for storing the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir, self.args.env1_name + self.args.env2_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
# check whether to continue training or start new
if args.continue_training is None:
self.continueTraining = False
else:
self.continueTraining = args.continue_training
# create the network
self.actor_network = actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
self.actor_target_network = actor(env_params)
self.critic_target_network = critic(env_params)
# build up the target network
dash = "-"*42
if MPI.COMM_WORLD.Get_rank() == 0:
print("env.spec.id: ", env.spec.id)
print("args: ")
d_args = vars(args)
print(dash)
print("{:<25s}{:<15s}".format("ARGS", "VALUE"))
for key in d_args:
if d_args[key] is not None:
print("|{:<22s} | {:<15}|".format(key, d_args[key]))
print(dash)
print("env_inits: ")
print("{:<25s}{:<15s}".format("ENV_INIT", "VALUE"))
for key in env.env.inits:
print("|{:<22s} | {:<15}|".format(key, env.env.inits[key]))
print(dash)
print("env_dimensions: ")
for key in env_params:
print("|{:<22s} | {:<15}|".format(key, env_params[key]))
print(dash)
#print("env_params", env_params)
if self.continueTraining:
if MPI.COMM_WORLD.Get_rank() == 0:
print("CONTINUE TRAINING...")
env_name = env.spec.id
saved_dicts = load_saved_state_dicts(
args.save_dir, env_name, MPI.COMM_WORLD.Get_rank())
self.actor_network.load_state_dict(saved_dicts['actor'])
self.critic_network.load_state_dict(saved_dicts['critic'])
self.critic_target_network.load_state_dict(
saved_dicts['critic_target'])
self.actor_target_network.load_state_dict(
saved_dicts['actor_target'])
else:
# load the weights into the target networks
self.actor_target_network.load_state_dict(
self.actor_network.state_dict())
self.critic_target_network.load_state_dict(
self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network.cuda()
self.critic_network.cuda()
self.actor_target_network.cuda()
self.critic_target_network.cuda()
# create the optimizer
self.actor_optim = torch.optim.Adam(
self.actor_network.parameters(), lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(
self.critic_network.parameters(), lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(
self.args.replay_strategy, self.args.replay_k, self.env.compute_reward)
# create the replay buffer
self.buffer = replay_buffer(
self.env_params, self.args.buffer_size, self.her_module.sample_her_transitions)
# create the normalizer
self.o_norm = normalizer(
size=env_params['obs'], default_clip_range=self.args.clip_range)
self.g_norm = normalizer(
size=env_params['goal'], default_clip_range=self.args.clip_range)
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(
self.args.save_dir, self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
def __init__(self, args, env, env_params, writer=None):
if args.cuda:
torch.cuda.set_device(args.device)
self.args = args
self.env = env
env_params['action'] = env_params['action'] // 2
env_params['obs'] = 37 # original 56
self.env_params = env_params
# create the network
self.actor_network_1 = actor_prob(env_params)
self.critic_network_1 = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network_1)
sync_networks(self.critic_network_1)
# build up the target network
self.actor_target_network_1 = actor_prob(env_params)
self.critic_target_network_1 = critic(env_params)
# load the weights into the target networks
self.actor_target_network_1.load_state_dict(
self.actor_network_1.state_dict())
self.critic_target_network_1.load_state_dict(
self.critic_network_1.state_dict())
# create the network
self.actor_network_2 = actor_prob(env_params)
self.critic_network_2 = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network_2)
sync_networks(self.critic_network_2)
# build up the target network
self.actor_target_network_2 = actor_prob(env_params)
self.critic_target_network_2 = critic(env_params)
# load the weights into the target networks
self.actor_target_network_2.load_state_dict(
self.actor_network_2.state_dict())
self.critic_target_network_2.load_state_dict(
self.critic_network_2.state_dict())
# if use gpu
if self.args.cuda:
self.actor_network_1.to(args.device)
self.critic_network_1.to(args.device)
self.actor_target_network_1.to(args.device)
self.critic_target_network_1.to(args.device)
self.actor_network_2.to(args.device)
self.critic_network_2.to(args.device)
self.actor_target_network_2.to(args.device)
self.critic_target_network_2.to(args.device)
# create the optimizer
self.actor_optim_1 = torch.optim.Adam(
self.actor_network_1.parameters(), lr=self.args.lr_actor)
self.critic_optim_1 = torch.optim.Adam(
self.critic_network_1.parameters(), lr=self.args.lr_critic)
self.actor_optim_2 = torch.optim.Adam(
self.actor_network_2.parameters(), lr=self.args.lr_actor_2)
self.critic_optim_2 = torch.optim.Adam(
self.critic_network_2.parameters(), lr=self.args.lr_critic_2)
# her sampler
if 'Stack' in self.args.env_name or 'Lift' in self.args.env_name:
self.her_module_1 = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward,
stack=True)
self.her_module_2 = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward,
stack=True)
else:
self.her_module_1 = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward)
self.her_module_2 = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward)
# if args.only_for_door:
# self.her_module_2 = her_sampler(self.args.replay_strategy, self.args.replay_k,
# self.env.compute_reward_only_for_door, double=True)
# else:
# self.her_module_2 = her_sampler(self.args.replay_strategy, self.args.replay_k,
# self.env.compute_reward_for_door, double=True)
# create the replay buffer
self.buffer_1 = replay_buffer(self.env_params, self.args.buffer_size,
self.her_module_1.sample_her_transitions)
self.buffer_2 = replay_buffer(self.env_params, self.args.buffer_size,
self.her_module_2.sample_her_transitions)
# self.buffer_2 = replay_buffer(self.env_params, self.args.buffer_size, self.her_module_2.sample_her_transitions,
# double=True)
# create the normalizer
self.o_norm_1 = normalizer(
size=37, default_clip_range=self.args.clip_range) # original 56
self.o_norm_2 = normalizer(
size=37, default_clip_range=self.args.clip_range) # original 56
self.g_norm = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
# for visualization
self.g_norm_1 = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
self.g_norm_2 = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
self.explore = None
if args.count_exp:
self.explore = HashingBonusEvaluator(
obs_processed_flat_dim=37, beta=args.exp_beta) # original 56
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
if args.count_exp:
self.model_path = os.path.join(self.model_path,
'count-exploration')
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
# flag = 'actor_lr_' + str(self.args.lr_actor) + '_critic_lr_' + str(
# self.args.lr_critic) + '_actor_lr_2_' + str(self.args.lr_actor_2) + '_critic_lr_2_' + str(
# self.args.lr_critic_2)
# flag += '_shaped_reward'
# if args.only_for_door:
# flag += '_only_for_door'
# else:
# flag += '_not_only_for_door'
flag = str(self.args.env_name)
self.model_path = os.path.join(self.model_path, flag)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
if not os.path.exists(self.args.save_training_success_rate_dir):
os.mkdir(self.args.save_training_success_rate_dir)
self.training_success_rate_path = os.path.join(
self.args.save_training_success_rate_dir, self.args.env_name)
if not os.path.exists(self.training_success_rate_path):
os.mkdir(self.training_success_rate_path)
self.training_success_rate_path = os.path.join(
self.training_success_rate_path, flag)
if not os.path.exists(self.training_success_rate_path):
os.mkdir(self.training_success_rate_path)
if not os.path.exists(self.args.save_training_return_dir):
os.mkdir(self.args.save_training_return_dir)
self.training_return_path = os.path.join(
self.args.save_training_return_dir, self.args.env_name)
if not os.path.exists(self.training_return_path):
os.mkdir(self.training_return_path)
self.training_return_path = os.path.join(self.training_return_path,
flag)
if not os.path.exists(self.training_return_path):
os.mkdir(self.training_return_path)
self.writer = writer
# for sac, one more critic for each
self.critic_network_1_2 = critic(env_params)
self.critic_network_2_2 = critic(env_params)
sync_networks(self.critic_network_1_2)
sync_networks(self.critic_network_2_2)
self.critic_target_network_1_2 = critic(env_params)
self.critic_target_network_2_2 = critic(env_params)
# load the weights into the target networks
self.critic_target_network_1_2.load_state_dict(
self.critic_network_1_2.state_dict())
self.critic_target_network_2_2.load_state_dict(
self.critic_network_2_2.state_dict())
# if use gpu
if self.args.cuda:
self.critic_network_1_2.to(args.device)
self.critic_target_network_1_2.to(args.device)
self.critic_network_2_2.to(args.device)
self.critic_target_network_2_2.to(args.device)
# create the optimizer
self.critic_optim_1_2 = torch.optim.Adam(
self.critic_network_1_2.parameters(), lr=self.args.lr_critic)
self.critic_optim_2_2 = torch.optim.Adam(
self.critic_network_2_2.parameters(), lr=self.args.lr_critic_2)
self.alpha_1 = args.alpha # 0.2
self.alpha_2 = args.alpha # 0.2
single_action_space = (env.action_space.shape[0] // 2, )
if self.args.cuda:
self.target_entropy_1 = -torch.prod(
torch.FloatTensor(single_action_space).to(
self.args.device)).item()
if self.args.cuda:
self.log_alpha_1 = torch.zeros(1,
requires_grad=True,
device=self.args.device)
self.alpha_optim_1 = torch.optim.Adam(
[self.log_alpha_1],
lr=args.alpha_lr) # two agent share same alpha_lr now
if self.args.cuda:
self.target_entropy_2 = -torch.prod(
torch.FloatTensor(single_action_space).to(
self.args.device)).item()
if self.args.cuda:
self.log_alpha_2 = torch.zeros(1,
requires_grad=True,
device=self.args.device)
self.alpha_optim_2 = torch.optim.Adam(
[self.log_alpha_2],
lr=args.alpha_lr) # two agent share same alpha_lr now