def __init__(
self,
env,
replay_buffer: ReplayBuffer,
policy_net: torch.nn.Module, # actor
q_net1: torch.nn.Module, # critic
q_net2: torch.nn.Module,
policy_optimizer: torch.optim.Optimizer,
q_optimizer1: torch.optim.Optimizer,
q_optimizer2: torch.optim.Optimizer,
gamma=0.99,
tau=0.05,
alpha=0.5,
automatic_entropy_tuning=False,
explore_step=2000,
max_train_step=50000,
train_id="sac_test",
log_interval=1000,
resume=False):
self.env = env
self.replay_buffer = replay_buffer
# the network and optimizers
self.policy_net = policy_net
self.q_net1 = q_net1
self.q_net2 = q_net2
self.target_q_net1 = copy.deepcopy(self.q_net1)
self.target_q_net2 = copy.deepcopy(self.q_net2)
self.policy_optimizer = policy_optimizer
self.q_optimizer1 = q_optimizer1
self.q_optimizer2 = q_optimizer2
self.gamma = gamma
self.tau = tau
self.alpha = alpha
self.automatic_entropy_tuning = automatic_entropy_tuning
if self.automatic_entropy_tuning:
self.target_entropy = -np.prod(self.env.action_space.shape).item()
self.log_alpha = torch.zeros(1, requires_grad=True)
self.alpha_optimizer = torch.optim.Adam([self.log_alpha], lr=3e-3)
self.alpha = torch.exp(self.log_alpha)
self.explore_step = explore_step
self.max_train_step = max_train_step
self.train_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join("./results", train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env: Env,
trajectory_buffer: TrajectoryBuffer,
actor_net: torch.nn.Module,
critic_net: torch.nn.Module,
actor_lr=1e-4,
critic_lr=1e-3,
gamma=0.99,
gae_lambda=0.95,
gae_normalize=False,
clip_pram=0.2,
trajectory_length=128, # the length of a trajectory_
train_actor_iters=10,
train_critic_iters=10,
eval_freq=1000, # it will not evaluate the agent during train if eval_freq < 0
max_time_step=10000,
train_id="PPO_CarPole_test",
log_interval=1000,
resume=False, # if True, train from last checkpoint
device='cpu'):
self.env = env
self.trajectory_buffer = trajectory_buffer
self.device = torch.device(device)
# the network and optimizers
self.actor_net = actor_net.to(self.device)
self.critic_net = critic_net.to(self.device)
self.actor_optimizer = torch.optim.Adam(self.actor_net.parameters(),
lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic_net.parameters(),
lr=critic_lr)
self.gamma = gamma
self.gae_lambda = gae_lambda
self.gae_normalize = gae_normalize
self.trajectory_length = trajectory_length
self.train_actor_iters = train_actor_iters
self.train_critic_iters = train_critic_iters
self.clip_pram = clip_pram
self.eval_freq = eval_freq
self.max_time_step = max_time_step
self.time_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env: Env,
replay_buffer: ReplayBuffer,
actor_net: torch.nn.Module,
critic_net: torch.nn.Module,
actor_lr=1e-4,
critic_lr=1e-3,
gamma=0.99,
tau=0.005, # used to update target network, w' = tau*w + (1-tau)*w'
explore_step=128,
eval_freq=1000, # it will not evaluate the agent during train if eval_freq < 0
max_train_step=10000,
gaussian_noise_sigma=0.2,
train_id="ddpg_Pendulum_test",
log_interval=1000,
resume=False, # if True, train from last checkpoint
device='cpu'):
self.env = env
self.action_num = env.action_space.shape[0]
self.action_bound = env.action_space.high[0]
self.replay_buffer = replay_buffer
self.device = torch.device(device)
# the network and optimizers
self.actor_net = actor_net.to(self.device)
self.target_actor_net = copy.deepcopy(self.actor_net).to(self.device)
self.critic_net = critic_net.to(self.device)
self.target_critic_net = copy.deepcopy(self.critic_net).to(self.device)
self.actor_optimizer = torch.optim.Adam(self.actor_net.parameters(),
lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic_net.parameters(),
lr=critic_lr)
self.gamma = gamma
self.tau = tau
self.gaussian_noise_sigma = gaussian_noise_sigma
self.explore_step = explore_step
self.eval_freq = eval_freq
self.max_train_step = max_train_step
self.train_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env: Env,
replay_buffer: ReplayBuffer,
Q_net: torch.nn.Module,
qf_lr=0.001,
gamma=0.99,
initial_eps=0.1,
end_eps=0.001,
eps_decay_period=2000,
eval_eps=0.001,
target_update_freq=10,
train_interval: int = 1,
explore_step=500,
eval_freq=1000, # it will not evaluate the agent during train if eval_freq < 0
max_train_step=10000,
train_id="ddqn_CartPole_test",
log_interval=1000,
resume=False, # if True, train from last checkpoint
device='cpu'):
self.env = env
self.replay_buffer = replay_buffer
self.explore_step = explore_step
self.eval_freq = eval_freq
self.max_train_step = max_train_step
self.train_interval = train_interval
self.target_update_freq = target_update_freq
self.device = torch.device(device)
self.Q_net = Q_net.to(self.device)
self.target_Q_net = copy.deepcopy(self.Q_net).to(self.device)
self.optimizer = torch.optim.Adam(self.Q_net.parameters(), lr=qf_lr)
# Decay for epsilon
self.initial_eps = initial_eps
self.end_eps = end_eps
self.slope = (self.end_eps - self.initial_eps) / eps_decay_period
self.eval_eps = eval_eps
self.gamma = gamma
self.train_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(self,
env,
data_buffer,
Q_net: torch.nn.Module,
qf_lr=0.001,
gamma=0.99,
eval_eps=0.001,
target_update_freq=8000,
train_interval: int = 1,
# CQL
min_q_weight=5.0, # the value of alpha in CQL loss, set to 5.0 or 10.0 if not using lagrange
max_train_step=2000000,
log_interval=1000,
eval_freq=5000,
train_id="sac_Pendulum_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.data_buffer = data_buffer
self.max_train_step = max_train_step
self.train_interval = train_interval
self.target_update_freq = target_update_freq
self.device = torch.device(device)
self.Q_net = Q_net.to(self.device)
self.target_Q_net = copy.deepcopy(self.Q_net).to(self.device)
self.optimizer = torch.optim.Adam(self.Q_net.parameters(), lr=qf_lr)
self.min_q_weight= min_q_weight
self.eval_eps = eval_eps
self.gamma = gamma
self.train_step = 0
self.eval_freq = eval_freq
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results", train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env: Env,
replay_buffer: ReplayBuffer,
actor_net: torch.nn.Module,
critic_net: torch.nn.Module,
actor_optimizer: torch.optim.Optimizer,
critic_optimizer: torch.optim.Optimizer,
gamma=0.99,
tau=0.005, # used to update target network, w' = tau*w + (1-tau)*w'
explore_step=128,
max_train_step=10000,
gaussian_noise_sigma=0.2,
train_id="ddpg_Pendulum_test",
log_interval=1000,
resume=False):
self.env = env
self.action_num = env.action_space.shape[0]
self.action_bound = env.action_space.high[0]
self.replay_buffer = replay_buffer
# the network and optimizers
self.actor_net = actor_net
self.target_actor_net = copy.deepcopy(self.actor_net)
self.critic_net = critic_net
self.target_critic_net = copy.deepcopy(self.critic_net)
self.actor_optimizer = actor_optimizer
self.critic_optimizer = critic_optimizer
self.gamma = gamma
self.tau = tau
self.gaussian_noise_sigma = gaussian_noise_sigma
self.explore_step = explore_step
self.max_train_step = max_train_step
self.train_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join("./results", train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(self,
env: Env,
replay_buffer: ReplayBuffer,
Q_net: torch.nn.Module,
optimizer: torch.optim.Optimizer,
gamma=0.99,
initial_eps=0.1,
end_eps=0.001,
eps_decay_period=25e4,
eval_eps=0.001,
target_update_freq=10,
train_interval: int = 1,
explore_step=500,
max_train_step=10000,
train_id="dqn_CartPole_test",
log_interval=1000,
resume=False):
self.env = env
self.replay_buffer = replay_buffer
self.explore_step = explore_step
self.max_train_step = max_train_step
self.train_interval = train_interval
self.target_update_freq = target_update_freq
self.Q_net = Q_net
self.target_Q_net = copy.deepcopy(self.Q_net)
self.optimizer = optimizer
# Decay for epsilon
self.initial_eps = initial_eps
self.end_eps = end_eps
self.slope = (self.end_eps - self.initial_eps) / eps_decay_period
self.eval_eps = eval_eps
self.gamma = gamma
self.train_step = 0
self.episode_num = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join("./results", train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env,
replay_buffer: OfflineBuffer,
actor_net: torch.nn.Module,
critic_net1: torch.nn.Module,
critic_net2: torch.nn.Module,
actor_lr=3e-4,
critic_lr=3e-4,
gamma=0.99,
tau=0.005, # used to update target network, w' = tau*w + (1-tau)*w'
policy_noise=0.2, # Noise added to target policy during critic update
noise_clip=0.5, # Range to clip target policy noise
policy_delay=2, # Frequency of delayed policy updates
alpha=2.5, # The alpha to compute lambda
max_train_step=1000000,
log_interval=1000,
eval_freq=5000,
train_id="td3bc_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.action_num = env.action_space.shape[0]
self.action_bound = env.action_space.high[0]
self.replay_buffer = replay_buffer
self.device = torch.device(device)
# the network and optimizers
self.actor_net = actor_net.to(self.device)
self.target_actor_net = copy.deepcopy(self.actor_net).to(self.device)
self.critic_net1 = critic_net1.to(self.device)
self.target_critic_net1 = copy.deepcopy(self.critic_net1).to(
self.device)
self.critic_net2 = critic_net2.to(self.device)
self.target_critic_net2 = copy.deepcopy(self.critic_net2).to(
self.device)
self.actor_optimizer = torch.optim.Adam(self.actor_net.parameters(),
lr=actor_lr)
self.critic_optimizer1 = torch.optim.Adam(
self.critic_net1.parameters(), lr=critic_lr)
self.critic_optimizer2 = torch.optim.Adam(
self.critic_net2.parameters(), lr=critic_lr)
self.gamma = gamma
self.tau = tau
self.policy_noise = policy_noise
self.noise_clip = noise_clip
self.policy_delay = policy_delay
self.alpha = alpha
self.actor_loss = 0
self.eval_freq = eval_freq
self.max_train_step = max_train_step
self.train_step = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env,
data_buffer: OfflineBuffer,
policy_net: MLPSquashedReparamGaussianPolicy, # actor
q_net1: MLPQsaNet, # critic
q_net2: MLPQsaNet,
cvae_net: CVAE,
policy_lr=1e-4,
qf_lr=3e-4,
cvae_lr=3e-4,
gamma=0.99,
tau=0.05,
# BEAR
lmbda=0.75, # used for double clipped double q-learning
mmd_sigma=20.0, # the sigma used in mmd kernel
kernel_type='gaussian', # the type of mmd kernel(gaussian or laplacian)
lagrange_thresh=0.05, # the hyper-parameter used in automatic tuning alpha in cql loss
n_action_samples=100, # the number of action samples to compute the best action when choose action
n_target_samples=10, # the number of action samples to compute BCQ-like target value
n_mmd_action_samples=4, # the number of action samples to compute MMD.
warmup_step=40000, # do support matching with a warm start before policy(actor) train
max_train_step=1000000,
log_interval=1000,
eval_freq=5000,
train_id="bear_hopper-medium-v2_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.data_buffer = data_buffer
self.device = torch.device(device)
# the network and optimizers
self.policy_net = policy_net.to(self.device)
self.q_net1 = q_net1.to(self.device)
self.q_net2 = q_net2.to(self.device)
self.target_q_net1 = copy.deepcopy(self.q_net1).to(self.device)
self.target_q_net2 = copy.deepcopy(self.q_net2).to(self.device)
self.cvae_net = cvae_net.to(self.device)
self.policy_optimizer = torch.optim.Adam(self.policy_net.parameters(),
lr=policy_lr)
self.q_optimizer1 = torch.optim.Adam(self.q_net1.parameters(),
lr=qf_lr)
self.q_optimizer2 = torch.optim.Adam(self.q_net2.parameters(),
lr=qf_lr)
self.cvae_optimizer = torch.optim.Adam(self.cvae_net.parameters(),
lr=cvae_lr)
self.gamma = gamma
self.tau = tau
self.max_train_step = max_train_step
self.eval_freq = eval_freq
self.train_step = 0
self.resume = resume # whether load checkpoint start train from last time
# BEAR
self.lmbda = lmbda
self.mmd_sigma = mmd_sigma
self.kernel_type = kernel_type
self.lagrange_thresh = lagrange_thresh
self.n_action_samples = n_action_samples
self.n_target_samples = n_target_samples
self.n_mmd_action_samples = n_mmd_action_samples
self.warmup_step = warmup_step
# mmd loss's temperature
self.log_alpha_prime = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_prime_optimizer = torch.optim.Adam([self.log_alpha_prime],
lr=1e-3)
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(self,
env,
data_buffer: OfflineBuffer,
policy_net: torch.nn.Module, # actor
q_net1: torch.nn.Module, # critic
q_net2: torch.nn.Module,
policy_lr=3e-4,
qf_lr=3e-4,
gamma=0.99,
tau=0.05,
alpha=0.5,
auto_alpha_tuning=False,
# CQL
min_q_weight=5.0, # the value of alpha in CQL loss, set to 5.0 or 10.0 if not using lagrange
entropy_backup=False, # whether use sac style target Q with entropy
max_q_backup=False, # whether use max q backup
with_lagrange=False, # whether auto tune alpha in Conservative Q Loss(different from the alpha in sac)
lagrange_thresh=0.0, # the hyper-parameter used in automatic tuning alpha in cql loss
n_action_samples=10, # the number of action sampled in importance sampling
max_train_step=2000000,
log_interval=1000,
eval_freq=5000,
train_id="cql_hopper-medium-v2_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.data_buffer = data_buffer
self.device = torch.device(device)
# the network and optimizers
self.policy_net = policy_net.to(self.device)
self.q_net1 = q_net1.to(self.device)
self.q_net2 = q_net2.to(self.device)
self.target_q_net1 = copy.deepcopy(self.q_net1).to(self.device)
self.target_q_net2 = copy.deepcopy(self.q_net2).to(self.device)
self.policy_optimizer = torch.optim.Adam(self.policy_net.parameters(), lr=policy_lr)
self.q_optimizer1 = torch.optim.Adam(self.q_net1.parameters(), lr=qf_lr)
self.q_optimizer2 = torch.optim.Adam(self.q_net2.parameters(), lr=qf_lr)
self.gamma = gamma
self.tau = tau
self.alpha = alpha
self.auto_alpha_tuning = auto_alpha_tuning
if self.auto_alpha_tuning:
self.target_entropy = -np.prod(self.env.action_space.shape).item()
self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
self.alpha_optimizer = torch.optim.Adam([self.log_alpha], lr=policy_lr)
self.alpha = torch.exp(self.log_alpha)
self.max_train_step = max_train_step
self.eval_freq = eval_freq
self.train_step = 0
self.resume = resume # whether load checkpoint start train from last time
# CQL
self.min_q_weight = min_q_weight
self.entropy_backup = entropy_backup
self.max_q_backup = max_q_backup
self.with_lagrange = with_lagrange
self.lagrange_thresh = lagrange_thresh
self.n_action_samples = n_action_samples
if self.with_lagrange:
self.log_alpha_prime = torch.zeros(1, requires_grad=True, device=self.device)
self.alpha_prime_optimizer = torch.optim.Adam([self.log_alpha_prime], lr=qf_lr)
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results", train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env,
data_buffer: OfflineBuffer,
critic_net1: MLPQsaNet,
critic_net2: MLPQsaNet,
actor_net: PLAS_Actor,
cvae_net: CVAE, # generation model
critic_lr=1e-3,
actor_lr=1e-4,
cvae_lr=1e-4,
gamma=0.99,
tau=0.005,
lmbda=0.75, # used for double clipped double q-learning
max_cvae_iterations=500000, # the num of iterations when training CVAE model
max_train_step=2000000,
log_interval=1000,
eval_freq=5000,
train_id="plas_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.data_buffer = data_buffer
self.device = torch.device(device)
self.critic_net1 = critic_net1.to(self.device)
self.critic_net2 = critic_net2.to(self.device)
self.target_critic_net1 = copy.deepcopy(self.critic_net1).to(
self.device)
self.target_critic_net2 = copy.deepcopy(self.critic_net2).to(
self.device)
self.actor_net = actor_net.to(self.device)
self.target_actor_net = copy.deepcopy(self.actor_net).to(self.device)
self.cvae_net = cvae_net.to(self.device)
self.critic_optimizer1 = torch.optim.Adam(
self.critic_net1.parameters(), lr=critic_lr)
self.critic_optimizer2 = torch.optim.Adam(
self.critic_net2.parameters(), lr=critic_lr)
self.actor_optimizer = torch.optim.Adam(self.actor_net.parameters(),
lr=actor_lr)
self.cvae_optimizer = torch.optim.Adam(self.cvae_net.parameters(),
lr=cvae_lr)
self.gamma = gamma
self.tau = tau
self.lmbda = lmbda
self.max_cvae_iterations = max_cvae_iterations
self.max_train_step = max_train_step
self.eval_freq = eval_freq
self.cvae_iterations = 0
self.train_step = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
def __init__(
self,
env,
data_buffer: OfflineBuffer,
policy_net: torch.nn.Module, # actor
q_net1: torch.nn.Module, # critic
q_net2: torch.nn.Module,
policy_lr=3e-4,
qf_lr=3e-4,
gamma=0.99,
tau=0.05,
alpha=0.5,
auto_alpha_tuning=False,
max_train_step=2000000,
log_interval=1000,
eval_freq=5000,
train_id="sac_Pendulum_test",
resume=False, # if True, train from last checkpoint
device='cpu',
):
self.env = env
self.data_buffer = data_buffer
self.device = torch.device(device)
# the network and optimizers
self.policy_net = policy_net.to(self.device)
self.q_net1 = q_net1.to(self.device)
self.q_net2 = q_net2.to(self.device)
self.target_q_net1 = copy.deepcopy(self.q_net1).to(self.device)
self.target_q_net2 = copy.deepcopy(self.q_net2).to(self.device)
self.policy_optimizer = torch.optim.Adam(self.policy_net.parameters(),
lr=policy_lr)
self.q_optimizer1 = torch.optim.Adam(self.q_net1.parameters(),
lr=qf_lr)
self.q_optimizer2 = torch.optim.Adam(self.q_net2.parameters(),
lr=qf_lr)
self.gamma = gamma
self.tau = tau
self.alpha = alpha
self.auto_alpha_tuning = auto_alpha_tuning
if self.auto_alpha_tuning:
self.target_entropy = -np.prod(self.env.action_space.shape).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optimizer = torch.optim.Adam([self.log_alpha],
lr=policy_lr)
self.alpha = torch.exp(self.log_alpha)
self.max_train_step = max_train_step
self.eval_freq = eval_freq
self.train_step = 0
self.resume = resume # whether load checkpoint start train from last time
# log dir and interval
self.log_interval = log_interval
self.result_dir = os.path.join(log_tools.ROOT_DIR, "run/results",
train_id)
log_tools.make_dir(self.result_dir)
self.checkpoint_path = os.path.join(self.result_dir, "checkpoint.pth")
self.tensorboard_writer = log_tools.TensorboardLogger(self.result_dir)
