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 = BaseReplayBuffer(
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.FixGuassianContPolicy(
input_shape=env.observation_space.shape[0],
output_shape=env.action_space.shape[0],
**params['net'],
**params['policy'])
qf = networks.QNet(input_shape=env.observation_space.shape[0] +
env.action_space.shape[0],
output_shape=1,
**params['net'])
print(pf)
print(qf)
params['general_setting']['collector'] = VecCollector(
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 = DDPG(pf=pf, qf=qf, **params["ddpg"], **params["general_setting"])
agent.train()
def experiment(args):
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)
if args.cuda:
torch.backends.cudnn.deterministic = True
buffer_param = params['replay_buffer']
replay_buffer = BaseReplayBuffer(int(buffer_param['size']))
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
params['general_setting']['replay_buffer'] = replay_buffer
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type'] = networks.MLPBase
# agent = get_agent( params )
# print(env)
# params['general_setting']['collector'] = BaseCollector(
# env, pf, replay_buffer
# )
pf = policies.GuassianContPolicy(
input_shape=env.observation_space.shape[0],
output_shape=2 * env.action_space.shape[0],
**params['net'])
vf = networks.Net(input_shape=env.observation_space.shape[0],
output_shape=1,
**params['net'])
qf = networks.FlattenNet(input_shape=env.observation_space.shape[0] +
env.action_space.shape[0],
output_shape=1,
**params['net'])
pretrain_pf = policies.UniformPolicyContinuous(env.action_space.shape[0])
params['general_setting']['collector'] = BaseCollector(env,
pf,
replay_buffer,
device=device)
params['general_setting']['save_dir'] = osp.join(logger.work_dir, "model")
agent = SAC(pf=pf,
vf=vf,
qf=qf,
pretrain_pf=pretrain_pf,
**params['sac'],
**params['general_setting'])
agent.train()
def experiment(args):
device = torch.device(
"cuda:{}".format(args.device) if args.cuda else "cpu")
env, cls_dicts, cls_args = get_meta_env(params['env_name'], params['env'],
params['meta_env'])
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
if args.cuda:
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
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type'] = networks.MLPBase
import torch.multiprocessing as mp
mp.set_start_method('spawn', force=True)
# from torchrl.networks.init import normal_init
example_ob = env.reset() # reset task_id as well
example_embedding = env.active_task_one_hot
pf = policies.ModularGuassianGatedCascadeCondContPolicy(
input_shape=env.observation_space.shape[0],
em_input_shape=np.prod(example_embedding.shape),
output_shape=2 * env.action_space.shape[0],
**params['net'])
if args.pf_snap is not None:
pf.load_state_dict(torch.load(args.pf_snap, map_location='cpu'))
qf1 = networks.FlattenModularGatedCascadeCondNet(
input_shape=env.observation_space.shape[0] + env.action_space.shape[0],
em_input_shape=np.prod(example_embedding.shape),
output_shape=1,
**params['net'])
qf2 = networks.FlattenModularGatedCascadeCondNet(
input_shape=env.observation_space.shape[0] + env.action_space.shape[0],
em_input_shape=np.prod(example_embedding.shape),
output_shape=1,
**params['net'])
if args.qf1_snap is not None:
qf1.load_state_dict(torch.load(args.qf2_snap, map_location='cpu'))
if args.qf2_snap is not None:
qf2.load_state_dict(torch.load(args.qf2_snap, map_location='cpu'))
example_dict = {
"obs": example_ob,
"next_obs": example_ob,
"acts": env.action_space.sample(),
"rewards": [0],
"terminals": [False],
"task_idxs": [0],
"embedding_inputs": example_embedding
}
replay_buffer = AsyncSharedReplayBuffer(int(buffer_param['size']),
args.worker_nums)
replay_buffer.build_by_example(example_dict)
params['general_setting']['replay_buffer'] = replay_buffer
epochs = params['general_setting']['pretrain_epochs'] + \
params['general_setting']['num_epochs']
print(env.action_space)
print(env.observation_space)
params['general_setting'][
'collector'] = AsyncMultiTaskParallelCollectorUniform(
env=env,
pf=pf,
replay_buffer=replay_buffer,
env_cls=cls_dicts,
env_args=[params["env"], cls_args, params["meta_env"]],
device=device,
reset_idx=True,
epoch_frames=params['general_setting']['epoch_frames'],
max_episode_frames=params['general_setting']['max_episode_frames'],
eval_episodes=params['general_setting']['eval_episodes'],
worker_nums=args.worker_nums,
eval_worker_nums=args.eval_worker_nums,
train_epochs=epochs,
eval_epochs=params['general_setting']['num_epochs'])
params['general_setting']['batch_size'] = int(
params['general_setting']['batch_size'])
params['general_setting']['save_dir'] = osp.join(logger.work_dir, "model")
agent = MTSAC(pf=pf,
qf1=qf1,
qf2=qf2,
task_nums=env.num_tasks,
**params['sac'],
**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)
if args.cuda:
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']))
# example_ob = env.reset()
# example_dict = {
# "obs": example_ob,
# "next_obs": example_ob,
# "acts": env.action_space.sample(),
# "values": [0],
# "rewards": [0],
# "terminals": [False]
# }
replay_buffer = OnPolicyReplayBuffer( int(buffer_param['size']))
# replay_buffer.build_by_example(example_dict)
params['general_setting']['replay_buffer'] = replay_buffer
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type']=networks.MLPBase
pf = policies.CategoricalDisPolicy(
input_shape = env.observation_space.shape[0],
output_shape = env.action_space.n,
**params['net'],
**params['policy']
)
vf = networks.Net(
input_shape = env.observation_space.shape,
output_shape = 1,
**params['net']
)
params['general_setting']['collector'] = OnPlicyCollectorBase(
vf, env = env, pf = pf, replay_buffer = replay_buffer, device = "cuda",
train_render=False
)
# params['general_setting']['collector'] = ParallelOnPlicyCollector(
# vf, env = env, pf = pf, replay_buffer = replay_buffer, device=device, worker_nums=2
# )
params['general_setting']['save_dir'] = osp.join(logger.work_dir,"model")
agent = PPO(
pf = pf,
vf = vf,
**params["ppo"],
**params["general_setting"]
)
agent.train()
def experiment(args):
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)
if args.cuda:
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
params['general_setting']['logger'] = logger
params['general_setting']['device'] = device
params['net']['base_type']=networks.MLPBase
import torch.multiprocessing as mp
mp.set_start_method('spawn')
pf = policies.GuassianContPolicy (
input_shape = env.observation_space.shape[0],
output_shape = 2 * env.action_space.shape[0],
**params['net'] )
vf = networks.Net(
input_shape = env.observation_space.shape[0],
output_shape = 1,
**params['net'] )
qf = networks.FlattenNet(
input_shape = env.observation_space.shape[0] + env.action_space.shape[0],
output_shape = 1,
**params['net'] )
# pretrain_pf = policies.UniformPolicyContinuous(env.action_space.shape[0])
example_ob = env.reset()
example_dict = {
"obs": example_ob,
"next_obs": example_ob,
"acts": env.action_space.sample(),
"rewards": [0],
"terminals": [False]
}
replay_buffer = SharedBaseReplayBuffer( int(buffer_param['size']),
1
)
replay_buffer.build_by_example(example_dict)
params['general_setting']['replay_buffer'] = replay_buffer
params['general_setting']['collector'] = ParallelCollector(
env, pf, replay_buffer, device=device, worker_nums=1
)
params['general_setting']['save_dir'] = osp.join(logger.work_dir,"model")
agent = SAC(
pf = pf,
vf = vf,
qf = qf,
**params['sac'],
**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 = VecEnv(4, 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']))
# example_ob = env.reset()
# example_dict = {
# "obs": example_ob,
# "next_obs": example_ob,
# "acts": env.action_space.sample(),
# "values": [0],
# "rewards": [0],
# "terminals": [False]
# }
# replay_buffer = SharedOnPolicyReplayBuffer( int(buffer_param['size']),
# args.worker_nums
# )
# replay_buffer.build_by_example(example_dict)
replay_buffer = VecOnPolicyReplayBuffer(
env_nums=2,
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.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['general_setting']['collector'] = VecOnPlicyCollector(
vf,
env=env,
pf=pf,
replay_buffer=replay_buffer,
device=device,
train_render=False,
epoch_frames=params["general_setting"]["epoch_frames"] // 2)
params['general_setting']['save_dir'] = osp.join(logger.work_dir, "model")
agent = PPO(pf=pf, vf=vf, **params["ppo"], **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()
