def run(env: gym.Env, args: argparse.Namespace, state_dim: int,
action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
# create multiple envs
env_single = env
env_gen = env_generator("LunarLanderContinuous-v2", args)
env_multi = make_envs(env_gen, n_envs=hyper_params["N_WORKERS"])
# create models
hidden_sizes_actor = [256, 256]
hidden_sizes_critic = [256, 256]
actor = GaussianDist(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
hidden_activation=torch.tanh,
).to(device)
critic = MLP(
input_size=state_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
hidden_activation=torch.tanh,
).to(device)
# create optimizer
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic.parameters(),
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# make tuples to create an agent
models = (actor, critic)
optims = (actor_optim, critic_optim)
# create an agent
agent = Agent(env_single, env_multi, args, hyper_params, models, optims)
# run
if args.test:
agent.test()
else:
agent.train()
def run(env: gym.Env, args: argparse.Namespace, state_dim: int,
action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
# create models
actor = GaussianDist(input_size=state_dim,
output_size=action_dim,
hidden_sizes=[256, 256]).to(device)
critic = MLP(input_size=state_dim, output_size=1,
hidden_sizes=[256, 256]).to(device)
# create optimizer
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic.parameters(),
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# make tuples to create an agent
models = (actor, critic)
optims = (actor_optim, critic_optim)
# create an agent
agent = Agent(env, args, hyper_params, models, optims)
# run
if args.test:
agent.test()
else:
agent.train()
def run(env: gym.Env, args: argparse.Namespace, state_dim: int,
action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
hidden_sizes_actor = [400, 300]
hidden_sizes_critic = [400, 300]
# create actor
actor = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target.load_state_dict(actor.state_dict())
# create critic
critic1 = FlattenMLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic2 = FlattenMLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1 = FlattenMLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target2 = FlattenMLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1.load_state_dict(critic1.state_dict())
critic_target2.load_state_dict(critic2.state_dict())
# concat critic parameters to use one optim
critic_parameters = list(critic1.parameters()) + list(critic2.parameters())
# create optimizers
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic_parameters,
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# noise instance to make randomness of action
exploration_noise = GaussianNoise(action_dim,
hyper_params["EXPLORATION_NOISE"],
hyper_params["EXPLORATION_NOISE"])
target_policy_noise = GaussianNoise(
action_dim,
hyper_params["TARGET_POLICY_NOISE"],
hyper_params["TARGET_POLICY_NOISE"],
)
# make tuples to create an agent
models = (actor, actor_target, critic1, critic2, critic_target1,
critic_target2)
optims = (actor_optim, critic_optim)
# create an agent
agent = TD3Agent(env, args, hyper_params, models, optims,
exploration_noise, target_policy_noise)
# run
if args.test:
agent.test()
else:
agent.train()
def get(env, args):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
"""
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
if hyper_params["USE_HER"]:
state_dim *= 2
hidden_sizes_actor = hyper_params["NETWORK"]["ACTOR_HIDDEN_SIZES"]
hidden_sizes_vf = hyper_params["NETWORK"]["VF_HIDDEN_SIZES"]
hidden_sizes_qf = hyper_params["NETWORK"]["QF_HIDDEN_SIZES"]
# target entropy
target_entropy = -np.prod((action_dim, )).item() # heuristic
# create actor
actor = TanhGaussianDistParams(input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor).to(device)
# create v_critic
vf = MLP(input_size=state_dim, output_size=1,
hidden_sizes=hidden_sizes_vf).to(device)
vf_target = MLP(input_size=state_dim,
output_size=1,
hidden_sizes=hidden_sizes_vf).to(device)
vf_target.load_state_dict(vf.state_dict())
# create q_critic
qf_1 = FlattenMLP(input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_qf).to(device)
qf_2 = FlattenMLP(input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_qf).to(device)
# create optimizers
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
vf_optim = optim.Adam(
vf.parameters(),
lr=hyper_params["LR_VF"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
qf_1_optim = optim.Adam(
qf_1.parameters(),
lr=hyper_params["LR_QF1"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
qf_2_optim = optim.Adam(
qf_2.parameters(),
lr=hyper_params["LR_QF2"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# make tuples to create an agent
models = (actor, vf, vf_target, qf_1, qf_2)
optims = (actor_optim, vf_optim, qf_1_optim, qf_2_optim)
# HER
her = LunarLanderContinuousHER() if hyper_params["USE_HER"] else None
# create an agent
return Agent(env, args, hyper_params, models, optims, target_entropy, her)
def get(env, args):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
"""
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
hidden_sizes_actor = hyper_params["NETWORK"]["ACTOR_HIDDEN_SIZES"]
hidden_sizes_critic = hyper_params["NETWORK"]["CRITIC_HIDDEN_SIZES"]
# create actor
actor = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target.load_state_dict(actor.state_dict())
# create critic1
critic1 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic1_target = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic1_target.load_state_dict(critic1.state_dict())
# create critic2
critic2 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic2_target = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic2_target.load_state_dict(critic2.state_dict())
# concat critic parameters to use one optim
critic_parameters = list(critic1.parameters()) + list(critic2.parameters())
# create optimizer
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic_parameters,
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# noise
exploration_noise = GaussianNoise(
action_dim,
min_sigma=hyper_params["EXPLORATION_NOISE"],
max_sigma=hyper_params["EXPLORATION_NOISE"],
)
target_policy_noise = GaussianNoise(
action_dim,
min_sigma=hyper_params["TARGET_POLICY_NOISE"],
max_sigma=hyper_params["TARGET_POLICY_NOISE"],
)
# make tuples to create an agent
models = (actor, actor_target, critic1, critic1_target, critic2,
critic2_target)
optims = (actor_optim, critic_optim)
noises = (exploration_noise, target_policy_noise)
# create an agent
return Agent(env, args, hyper_params, models, optims, noises)
def run(env: gym.Env, args: argparse.Namespace, state_dim: int,
action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
hidden_sizes_actor = [256, 256]
hidden_sizes_vf = [256, 256]
hidden_sizes_qf = [256, 256]
# target entropy
target_entropy = -np.prod((action_dim, )).item() # heuristic
# create actor
actor = TanhGaussianDistParams(input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor).to(device)
# create v_critic
vf = MLP(input_size=state_dim, output_size=1,
hidden_sizes=hidden_sizes_vf).to(device)
vf_target = MLP(input_size=state_dim,
output_size=1,
hidden_sizes=hidden_sizes_vf).to(device)
vf_target.load_state_dict(vf.state_dict())
# create q_critic
qf_1 = FlattenMLP(input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_qf).to(device)
qf_2 = FlattenMLP(input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_qf).to(device)
# create optimizers
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["LAMBDA2"],
)
vf_optim = optim.Adam(vf.parameters(),
lr=hyper_params["LR_VF"],
weight_decay=hyper_params["LAMBDA2"])
qf_1_optim = optim.Adam(
qf_1.parameters(),
lr=hyper_params["LR_QF1"],
weight_decay=hyper_params["LAMBDA2"],
)
qf_2_optim = optim.Adam(
qf_2.parameters(),
lr=hyper_params["LR_QF2"],
weight_decay=hyper_params["LAMBDA2"],
)
# make tuples to create an agent
models = (actor, vf, vf_target, qf_1, qf_2)
optims = (actor_optim, vf_optim, qf_1_optim, qf_2_optim)
# create an agent
agent = SACfDAgent(env, args, hyper_params, models, optims, target_entropy)
# run
if args.test:
agent.test()
else:
agent.train()
def run(env: gym.Env, args: argparse.Namespace, state_dim: int, action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
hidden_sizes_actor = [256, 256]
hidden_sizes_critic = [256, 256]
# create actor
actor = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target.load_state_dict(actor.state_dict())
# create critic
critic = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target.load_state_dict(critic.state_dict())
# create optimizer
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic.parameters(),
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# noise
noise = OUNoise(
action_dim,
theta=hyper_params["OU_NOISE_THETA"],
sigma=hyper_params["OU_NOISE_SIGMA"],
)
# make tuples to create an agent
models = (actor, actor_target, critic, critic_target)
optims = (actor_optim, critic_optim)
# HER
her = ReacherHER() if hyper_params["USE_HER"] else None
# create an agent
agent = BCDDPGAgent(env, args, hyper_params, models, optims, noise, her)
# run
if args.test:
agent.test()
else:
agent.train()
def run(env: gym.Env, args: argparse.Namespace, state_dim: int, action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
hidden_sizes_actor = [400, 300]
hidden_sizes_critic = [400, 300]
# create actor
actor = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target.load_state_dict(actor.state_dict())
# create critic
critic_1 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_2 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target2 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1.load_state_dict(critic_1.state_dict())
critic_target2.load_state_dict(critic_2.state_dict())
# create optimizers
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_parameter = list(critic_1.parameters()) + list(critic_2.parameters())
critic_optim = optim.Adam(
critic_parameter,
lr=hyper_params["LR_CRITIC_1"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# noise instance to make randomness of action
noise = GaussianNoise(
hyper_params["GAUSSIAN_NOISE_MIN_SIGMA"],
hyper_params["GAUSSIAN_NOISE_MAX_SIGMA"],
hyper_params["GAUSSIAN_NOISE_DECAY_PERIOD"],
)
# make tuples to create an agent
models = (actor, actor_target, critic_1, critic_2, critic_target1, critic_target2)
optims = (actor_optim, critic_optim)
# create an agent
agent = Agent(env, args, hyper_params, models, optims, noise)
# run
if args.test:
agent.test()
else:
agent.train()
def run(env: gym.Env,
env_name: str,
args: argparse.Namespace,
state_dim=75,
action_dim=17):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
# create models
actor = CategoricalDist(input_size=state_dim,
output_size=action_dim,
hidden_sizes=[256, 256, 128]).to(device)
critic = MLP(input_size=state_dim,
output_size=1,
hidden_sizes=[256, 256, 128]).to(device)
# create optimizer
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
critic_optim = optim.Adam(
critic.parameters(),
lr=hyper_params["LR_CRITIC"],
weight_decay=hyper_params["WEIGHT_DECAY"],
)
# make tuples to create an agent
models = (actor, critic)
optims = (actor_optim, critic_optim)
# create an agent
agent = A2CAgent(env, args, hyper_params, models, optims)
# run
if args.test:
all_names = [
'N3000', 'N3001', 'N3002', 'N3003', 'N3005', 'N3006', 'N3007',
'N3008', 'N300A', 'N300B', 'N300C', 'N300D', 'N300E', 'N3100',
'N3101', 'N3102', 'N3103', 'N3105', 'N3106', 'N3107', 'N3108',
'N310A', 'N310B', 'N310C', 'N310D', 'N310E', 'N3200', 'N3201',
'N3202', 'N3203', 'N3204', 'N3205', 'N3206', 'N3207', 'N3208',
'N3209', 'N320A', 'N320B', 'N320C', 'N320D', 'N320E', 'N3300',
'N3301', 'N3302', 'N3303', 'N3304', 'N3305', 'N3306', 'N3307',
'N3308', 'N3309', 'N330A', 'N330B', 'N330C', 'N330D', 'N330E',
'N3400', 'N3401', 'N3402', 'N3403', 'N3405', 'N3406', 'N3407',
'N3408', 'N340A', 'N340B', 'N340C', 'N340D', 'N340E', 'N3500',
'N3501', 'N3502', 'N3503', 'N3505', 'N3506', 'N3507', 'N3508',
'N350A', 'N350B', 'N350C', 'N350D', 'N350E', 'N3600', 'N3601',
'N3602', 'N3603', 'N3604', 'N3605', 'N3607', 'N3608', 'N360A',
'N360B', 'N360C', 'N360D', 'N360E', 'N3700', 'N3701', 'N3702',
'N3703', 'N3705', 'N3706', 'N3707', 'N3708', 'N370A', 'N370B',
'N370C', 'N370D', 'N370E'
]
for name in names:
agent.test(name='Glover/' + name + '.FCTP')
else:
agent.train()