class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.num_inputs = num_inputs
self.action_space = action_space.shape[0]
self.gamma = args.gamma
self.tau = args.tau
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.critic = QNetwork(self.num_inputs, self.action_space,
args.hidden_size)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
if self.policy_type == "Gaussian":
self.alpha = args.alpha
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape)).item()
self.log_alpha = torch.zeros(1, requires_grad=True)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
else:
pass
self.policy = GaussianPolicy(self.num_inputs, self.action_space,
args.hidden_size)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
self.value = ValueNetwork(self.num_inputs, args.hidden_size)
self.value_target = ValueNetwork(self.num_inputs, args.hidden_size)
self.value_optim = Adam(self.value.parameters(), lr=args.lr)
hard_update(self.value_target, self.value)
else:
self.policy = DeterministicPolicy(self.num_inputs,
self.action_space,
args.hidden_size)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
self.critic_target = QNetwork(self.num_inputs, self.action_space,
args.hidden_size)
hard_update(self.critic_target, self.critic)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).unsqueeze(0)
if eval == False:
self.policy.train()
action, _, _, _, _ = self.policy.sample(state)
else:
self.policy.eval()
_, _, _, action, _ = self.policy.sample(state)
if self.policy_type == "Gaussian":
action = torch.tanh(action)
else:
pass
#action = torch.tanh(action)
action = action.detach().cpu().numpy()
return action[0]
def update_parameters(self, state_batch, action_batch, reward_batch,
next_state_batch, mask_batch, updates):
state_batch = torch.FloatTensor(state_batch)
next_state_batch = torch.FloatTensor(next_state_batch)
action_batch = torch.FloatTensor(action_batch)
reward_batch = torch.FloatTensor(reward_batch).unsqueeze(1)
mask_batch = torch.FloatTensor(np.float32(mask_batch)).unsqueeze(1)
"""
Use two Q-functions to mitigate positive bias in the policy improvement step that is known
to degrade performance of value based methods. Two Q-functions also significantly speed
up training, especially on harder task.
"""
expected_q1_value, expected_q2_value = self.critic(
state_batch, action_batch)
new_action, log_prob, _, mean, log_std = self.policy.sample(
state_batch)
if self.policy_type == "Gaussian":
if self.automatic_entropy_tuning:
"""
Alpha Loss
"""
alpha_loss = -(
self.log_alpha *
(log_prob + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_logs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.)
alpha_logs = self.alpha # For TensorboardX logs
"""
Including a separate function approximator for the soft value can stabilize training.
"""
expected_value = self.value(state_batch)
target_value = self.value_target(next_state_batch)
next_q_value = reward_batch + mask_batch * self.gamma * (
target_value).detach()
else:
"""
There is no need in principle to include a separate function approximator for the state value.
We use a target critic network for deterministic policy and eradicate the value value network completely.
"""
alpha_loss = torch.tensor(0.)
alpha_logs = self.alpha # For TensorboardX logs
next_state_action, _, _, _, _, = self.policy.sample(
next_state_batch)
target_critic_1, target_critic_2 = self.critic_target(
next_state_batch, next_state_action)
target_critic = torch.min(target_critic_1, target_critic_2)
next_q_value = reward_batch + mask_batch * self.gamma * (
target_critic).detach()
"""
Soft Q-function parameters can be trained to minimize the soft Bellman residual
JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
∇JQ = ∇Q(st,at)(Q(st,at) - r(st,at) - γV(target)(st+1))
"""
q1_value_loss = F.mse_loss(expected_q1_value, next_q_value)
q2_value_loss = F.mse_loss(expected_q2_value, next_q_value)
q1_new, q2_new = self.critic(state_batch, new_action)
expected_new_q_value = torch.min(q1_new, q2_new)
if self.policy_type == "Gaussian":
"""
Including a separate function approximator for the soft value can stabilize training and is convenient to
train simultaneously with the other networks
Update the V towards the min of two Q-functions in order to reduce overestimation bias from function approximation error.
JV = 𝔼st~D[0.5(V(st) - (𝔼at~π[Qmin(st,at) - α * log π(at|st)]))^2]
∇JV = ∇V(st)(V(st) - Q(st,at) + (α * logπ(at|st)))
"""
next_value = expected_new_q_value - (self.alpha * log_prob)
value_loss = F.mse_loss(expected_value, next_value.detach())
else:
pass
"""
Reparameterization trick is used to get a low variance estimator
f(εt;st) = action sampled from the policy
εt is an input noise vector, sampled from some fixed distribution
Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
∇Jπ = ∇log π + ([∇at (α * logπ(at|st)) − ∇at Q(st,at)])∇f(εt;st)
"""
policy_loss = ((self.alpha * log_prob) - expected_new_q_value).mean()
# Regularization Loss
mean_loss = 0.001 * mean.pow(2).mean()
std_loss = 0.001 * log_std.pow(2).mean()
policy_loss += mean_loss + std_loss
self.critic_optim.zero_grad()
q1_value_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
q2_value_loss.backward()
self.critic_optim.step()
if self.policy_type == "Gaussian":
self.value_optim.zero_grad()
value_loss.backward()
self.value_optim.step()
else:
value_loss = torch.tensor(0.)
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
"""
We update the target weights to match the current value function weights periodically
Update target parameter after every n(args.target_update_interval) updates
"""
if updates % self.target_update_interval == 0 and self.policy_type == "Deterministic":
soft_update(self.critic_target, self.critic, self.tau)
elif updates % self.target_update_interval == 0 and self.policy_type == "Gaussian":
soft_update(self.value_target, self.value, self.tau)
return value_loss.item(), q1_value_loss.item(), q2_value_loss.item(
), policy_loss.item(), alpha_loss.item(), alpha_logs
# Save model parameters
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None,
value_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
if value_path is None:
value_path = "models/sac_value_{}_{}".format(env_name, suffix)
print('Saving models to {}, {} and {}'.format(actor_path, critic_path,
value_path))
torch.save(self.value.state_dict(), value_path)
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path, value_path):
print('Loading models from {}, {} and {}'.format(
actor_path, critic_path, value_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
if value_path is not None:
self.value.load_state_dict(torch.load(value_path))
class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.num_inputs = num_inputs
self.action_space = action_space.shape[0]
self.gamma = args.gamma
self.tau = args.tau
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(self.num_inputs, self.action_space,
args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
if self.policy_type == "Gaussian":
self.alpha = args.alpha
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(self.num_inputs, self.action_space,
args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
self.value = ValueNetwork(self.num_inputs,
args.hidden_size).to(self.device)
self.value_target = ValueNetwork(self.num_inputs,
args.hidden_size).to(self.device)
self.value_optim = Adam(self.value.parameters(), lr=args.lr)
hard_update(self.value_target, self.value)
else:
self.policy = DeterministicPolicy(self.num_inputs,
self.action_space,
args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
self.critic_target = QNetwork(self.num_inputs, self.action_space,
args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
self.policy.train()
action, _, _ = self.policy.sample(state)
else:
self.policy.eval()
_, _, action = self.policy.sample(state)
action = action.detach().cpu().numpy()
return action[0]
def update_parameters(self, state_batch, action_batch, reward_batch,
next_state_batch, mask_batch, updates):
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
pi, log_pi, _ = self.policy.sample(state_batch)
if self.policy_type == "Gaussian":
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_logs = torch.tensor(self.alpha) # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_logs = torch.tensor(self.alpha) # For TensorboardX logs
vf = self.value(
state_batch
) # separate function approximator for the soft value can stabilize training.
with torch.no_grad():
vf_next_target = self.value_target(next_state_batch)
next_q_value = reward_batch + mask_batch * self.gamma * (
vf_next_target)
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_logs = self.alpha # For TensorboardX logs
with torch.no_grad():
next_state_action, _, _, _, _, = self.policy.sample(
next_state_batch)
# Use a target critic network for deterministic policy and eradicate the value value network completely.
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(qf1_next_target,
qf2_next_target)
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
if self.policy_type == "Gaussian":
vf_target = min_qf_pi - (self.alpha * log_pi)
value_loss = F.mse_loss(
vf, vf_target.detach()
) # JV = 𝔼st~D[0.5(V(st) - (𝔼at~π[Qmin(st,at) - α * log π(at|st)]))^2]
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
# Regularization Loss
# mean_loss = 0.001 * mean.pow(2).mean()
# std_loss = 0.001 * log_std.pow(2).mean()
# policy_loss += mean_loss + std_loss
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
if self.policy_type == "Gaussian":
self.value_optim.zero_grad()
value_loss.backward()
self.value_optim.step()
else:
value_loss = torch.tensor(0.).to(self.device)
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
"""
We update the target weights to match the current value function weights periodically
Update target parameter after every n(args.target_update_interval) updates
"""
if updates % self.target_update_interval == 0 and self.policy_type == "Deterministic":
soft_update(self.critic_target, self.critic, self.tau)
elif updates % self.target_update_interval == 0 and self.policy_type == "Gaussian":
soft_update(self.value_target, self.value, self.tau)
return value_loss.item(), qf1_loss.item(), qf2_loss.item(
), policy_loss.item(), alpha_loss.item(), alpha_logs.item()
# Save model parameters
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None,
value_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
if value_path is None:
value_path = "models/sac_value_{}_{}".format(env_name, suffix)
print('Saving models to {}, {} and {}'.format(actor_path, critic_path,
value_path))
torch.save(self.value.state_dict(), value_path)
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path, value_path):
print('Loading models from {}, {} and {}'.format(
actor_path, critic_path, value_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
if value_path is not None:
self.value.load_state_dict(torch.load(value_path))
class SAC(object):
def __init__(self, num_inputs, action_space, variant):
self.gamma = variant['gamma']
self.tau = variant['tau']
self.alpha = variant['alpha']
self.policy_type = variant['policy_type']
self.target_update_interval = variant['target_update_interval']
self.automatic_entropy_tuning = variant['automatic_entropy_tuning']
self.lr = variant.get("lr", 1e-3)
self.device = torch.device("cuda" if variant['cuda'] else "cpu")
self.hidden_size = variant.get('hidden_size', [128, 128])
self.critic = QNetwork(num_inputs, action_space.shape[0],
self.hidden_size).to(self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=self.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
self.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
if self.policy_type == 'Gaussian':
if self.automatic_entropy_tuning:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=self.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
self.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=self.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
self.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=self.lr)
def select_action(self, state, evaluate=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if evaluate is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, updates):
#sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device)
mask_batch = torch.FloatTensor(mask_batch).to(self.device)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
# samle a batch of action and appropriate log_pi
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
# alpha_tlogs = self.alpha.clone()
else:
alpha_loss = torch.tensor(0.0).to(self.device)
if update % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item()
def save_model(self,
env_nam,
suffix=".pkl",
actor_path=None,
critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
print("Saving models to {} and {}".format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
def load_model(self, actor_path, critic_path):
print('loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.action_range = [action_space.low, action_space.high]
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
action = action.detach().cpu().numpy()[0]
return self.rescale_action(action)
def rescale_action(self, action):
return action * (self.action_range[1] - self.action_range[0]) / 2.0 +\
(self.action_range[1] + self.action_range[0]) / 2.0
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)
qf1, qf2 = self.critic(state_batch, action_batch) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(qf1, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(qf2, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self, suffix="", actor_path=None, critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/a_{}".format(suffix)
if critic_path is None:
critic_path = "models/c_{}".format(suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
class TD3Agent():
def __init__(self, s_dim, a_dim, action_space, args):
self.s_dim = s_dim
self.a_dim = a_dim
self.action_space = action_space
self.lr_pi = args.lr_pi
self.lr_q = args.lr_q
self.gamma = args.gamma
self.tau = args.tau
self.noise_std = args.noise_std
self.noise_clip = args.noise_clip
self.batch_size = args.batch_size
self.policy_update_interval = args.policy_update_interval
self.device = torch.device(args.device)
self.policy_loss_log = torch.tensor(0.).to(self.device)
self.policy = DeterministicPolicy(self.s_dim,
self.a_dim,
self.device,
action_space=self.action_space).to(
self.device)
self.policy_target = DeterministicPolicy(
self.s_dim,
self.a_dim,
self.device,
action_space=self.action_space).to(self.device)
self.Q1 = QFunction(self.s_dim, self.a_dim).to(self.device)
self.Q1_target = QFunction(self.s_dim, self.a_dim).to(self.device)
self.Q2 = QFunction(self.s_dim, self.a_dim).to(self.device)
self.Q2_target = QFunction(self.s_dim, self.a_dim).to(self.device)
self.hard_update_target()
self.optimizer_pi = optim.Adam(self.policy.parameters(), lr=self.lr_pi)
self.optimizer_q1 = optim.Adam(self.Q1.parameters(), lr=self.lr_q)
self.optimizer_q2 = optim.Adam(self.Q2.parameters(), lr=self.lr_q)
def hard_update_target(self):
self.policy_target.load_state_dict(self.policy.state_dict())
self.Q1_target.load_state_dict(self.Q1.state_dict())
self.Q2_target.load_state_dict(self.Q2.state_dict())
def soft_update_target(self):
for param, param_target in zip(self.policy.parameters(),
self.policy_target.parameters()):
param_target.data.copy_(param.data * self.tau + param_target.data *
(1 - self.tau))
for param, param_target in zip(self.Q1.parameters(),
self.Q1_target.parameters()):
param_target.data.copy_(param.data * self.tau + param_target.data *
(1 - self.tau))
for param, param_target in zip(self.Q2.parameters(),
self.Q2_target.parameters()):
param_target.data.copy_(param.data * self.tau + param_target.data *
(1 - self.tau))
def choose_action(self, s):
s = torch.from_numpy(s).to(self.device).float()
return self.policy.sample(s).cpu().detach().numpy()
def learn(self, memory, total_step):
s, a, r, s_, done = memory.sample_batch(self.batch_size)
s = torch.from_numpy(s).to(self.device)
a = torch.from_numpy(a).to(self.device)
r = torch.from_numpy(r).to(self.device).unsqueeze(dim=1)
s_ = torch.from_numpy(s_).to(self.device)
done = torch.from_numpy(done).to(self.device).unsqueeze(dim=1)
noise = (torch.randn_like(a) * self.noise_std).clamp(
-self.noise_clip, self.noise_clip)
a_target_next = self.policy_target.sample(s_) + noise
q1_next = self.Q1_target(s_, a_target_next)
q2_next = self.Q2_target(s_, a_target_next)
q_next_min = torch.min(q1_next, q2_next)
q_loss_target = r + (1 - done) * self.gamma * q_next_min
#update q1
q1_loss_pred = self.Q1(s, a)
q1_loss = F.mse_loss(q1_loss_pred, q_loss_target.detach()).mean()
self.optimizer_q1.zero_grad()
q1_loss.backward()
self.optimizer_q1.step()
#update q2
q2_loss_pred = self.Q2(s, a)
q2_loss = F.mse_loss(q2_loss_pred, q_loss_target.detach()).mean()
self.optimizer_q2.zero_grad()
q2_loss.backward()
self.optimizer_q2.step()
#delay upodate policy
if total_step % self.policy_update_interval == 0:
policy_loss = -self.Q1(s, self.policy.sample(s)).mean()
self.optimizer_pi.zero_grad()
policy_loss.backward()
self.optimizer_pi.step()
self.soft_update_target()
self.policy_loss_log = policy_loss
return q1_loss.item(), q2_loss.item(), self.policy_loss_log.item()
def save_model(self,
env_name,
remarks='',
pi_path=None,
q1_path=None,
q2_path=None):
if not os.path.exists('pretrained_models/'):
os.mkdir('pretrained_models/')
if pi_path == None:
pi_path = 'pretrained_models/policy_{}_{}'.format(
env_name, remarks)
if q1_path == None:
q1_path = 'pretrained_models/q1_{}_{}'.format(env_name, remarks)
if q2_path == None:
q2_path = 'pretrained_models/q2_{}_{}'.format(env_name, remarks)
print('Saving model to {} , {} and {}'.format(pi_path, q1_path,
q2_path))
torch.save(self.policy.state_dict(), pi_path)
torch.save(self.Q1.state_dict(), q1_path)
torch.save(self.Q2.state_dict(), q2_path)
def load_model(self, pi_path, q1_path, q2_path):
print('Loading models from {} , {} and {}'.format(
pi_path, q1_path, q2_path))
self.policy.load_state_dict(torch.load(pi_path))
self.Q1.load_state_dict(torch.load(q1_path))
self.Q2.load_state_dict(torch.load(q2_path))
class DDPGAgent():
def __init__(self, args, env_params):
self.o_dim = env_params['o_dim']
self.a_dim = env_params['a_dim']
self.action_boundary = env_params['action_boundary']
self.lr_a = args.lr_a
self.lr_c = args.lr_c
self.gamma = args.gamma
self.tau = args.tau
self.noise_eps = args.noise_eps
self.batch_size = args.batch_size
self.device = torch.device(args.device)
self.actor = DeterministicPolicy(self.o_dim,
self.a_dim).to(self.device)
self.actor_tar = DeterministicPolicy(self.o_dim,
self.a_dim).to(self.device)
self.critic = QFunction(self.o_dim, self.a_dim).to(self.device)
self.critic_tar = QFunction(self.o_dim, self.a_dim).to(self.device)
self.optimizer_a = optim.Adam(self.actor.parameters(), lr=self.lr_a)
self.optimizer_c = optim.Adam(self.critic.parameters(), lr=self.lr_c)
self.hard_update()
def hard_update(self):
self.actor_tar.load_state_dict(self.actor.state_dict())
self.critic_tar.load_state_dict(self.critic.state_dict())
def soft_update(self):
for params, params_tar in zip(self.actor.parameters(),
self.actor_tar.parameters()):
params_tar.data.copy_(self.tau * params.data +
(1 - self.tau) * params_tar.data)
for params, params_tar in zip(self.critic.parameters(),
self.critic_tar.parameters()):
params_tar.data.copy_(self.tau * params.data +
(1 - self.tau) * params_tar.data)
def choose_action(self, obs, is_evaluete=False):
obs = torch.from_numpy(obs).float().to(self.device)
with torch.no_grad():
action = self.actor(obs)
if not is_evaluete:
action += torch.normal(torch.tensor(0.),
torch.tensor(self.noise_eps))
action = torch.clamp(action, -self.action_boundary,
self.action_boundary).cpu().detach().numpy()
return action
def rollout(self, env, memory, is_evaluate=False):
total_reward = 0.
obs = env.reset()
done = False
while not done:
a = self.choose_action(obs, is_evaluate)
obs_, r, done, info = env.step(a)
memory.store(obs, a, r, obs_, done)
total_reward += r
obs = obs_
return total_reward
def update(self, memory):
obs, a, r, obs_, done = memory.sample_batch(self.batch_size)
obs = torch.from_numpy(obs).float().to(self.device)
a = torch.from_numpy(a).float().to(self.device)
r = torch.from_numpy(r).float().to(self.device)
obs_ = torch.from_numpy(obs_).float().to(self.device)
done = torch.from_numpy(done).float().to(self.device)
with torch.no_grad():
next_action_tar = self.actor_tar(obs_)
next_q_tar = self.critic_tar(obs_, next_action_tar)
critic_target = r + (1 - done) * self.gamma * next_q_tar
critic_eval = self.critic(obs, a)
loss_critic = F.mse_loss(critic_eval, critic_target.detach())
self.optimizer_c.zero_grad()
loss_critic.backward()
self.optimizer_c.step()
loss_actor = -self.critic(obs, self.actor(obs)).mean()
self.optimizer_a.zero_grad()
loss_actor.backward()
self.optimizer_a.step()
self.soft_update()
def save_model(self, remark):
if not os.path.exists('pretrained_model/'):
os.mkdir('pretrained_model/')
path = 'pretrained_model/{}.pt'.format(remark)
print('Saving model to {}'.format(path))
torch.save(self.actor.state_dict(), path)
def load_model(self, remark):
path = 'pretrained_model/{}.pt'.format(remark)
print('Loading model from {}'.format(path))
model = torch.load(path)
self.actor.load_state_dict(model)
class Agent(object):
def __init__(self, num_inputs, action_space, args):
self.args = args
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.alpha1 = args.alpha1
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
self.l = []
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, evaluate=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if evaluate is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_model1(self, model, new_params):
index = 0
for params in model.parameters():
params_length = len(params.view(-1))
new_param = new_params[index:index + params_length]
new_param = new_param.view(params.size())
params.data.copy_(new_param.to("cuda:0") + params.to("cuda:0"))
index += params_length
def update_parametersafter(self, memory, batch_size, updates, env, enco):
'''
Temporarily updates the parameters of the first agent.
Parameters
----------
memory : class 'replay_memory.ReplayMemory'
batch_size : int
updates : int
env : 'gym.wrappers.time_limit.TimeLimit'
The environment of interest
enco : class
The corresponding autoencoder
'''
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
cat = torch.cat((next_state_batch, next_state_action), dim=-1)
s = enco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
next_q_value = reward_batch + self.alpha * ll(
cat, s) + mask_batch * self.gamma * (min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = (-min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1)
self.policy_optim.step()
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
def update_parametersdeter(self, memory, batch_size, updates, env, enco):
'''
Updates the paratmeters of the second agent.
Parameters
----------
memory : class 'replay_memory.ReplayMemory'
batch_size : int
updates : int
env : 'gym.wrappers.time_limit.TimeLimit'
The environment of interest
enco : class
The corresponding autoencoder
'''
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
cat = torch.cat((state_batch, action_batch), dim=-1)
s = enco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
act, _, _ = self.policy.sample(state_batch)
next_q_value = reward_batch - self.alpha1 * (
(ll(cat, s) - torch.min(ll(cat, s))) /
(torch.max(ll(cat, s)) - torch.min(ll(cat, s)))) * ll(
act, action_batch) + mask_batch * self.gamma * (
min_qf_next_target) #refer to the paper
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = (
-min_qf_pi).mean() # J_{π_2} = 𝔼st∼D,εt∼N[− Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1)
self.policy_optim.step()
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
def X(self,
ss,
a,
memory,
batch_size,
updates,
env,
enco,
Qdac,
pidac,
QTdac,
args,
tenco,
normalization=False):
'''
Updates the parameters of the first agent (with the influence function and intrinsic rewards).
Parameters
----------
ss : numpy array
Current state
a : numpy array
Action taken in ss
memory : class 'replay_memory.ReplayMemory'
batch_size : int
updates : int
env : 'gym.wrappers.time_limit.TimeLimit'
The environment of interest
enco :
The corresponding autoencoder
Qdac :
The critic network of the second agent.
pidac :
The policy network of the second agent.
QTdac :
The target critic network of the second agent.
args :
Hyperparameters determined by the user.
tenco :
A virtual/proxy autoencoder used to calculate the frequency of (ss,a) w.r.t first agent's policy
'''
Qdac_optim = Adam(Qdac.parameters(), lr=args.lr)
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
cat = torch.cat((next_state_batch, next_state_action), dim=-1)
s = enco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
if normalization:
next_q_value = reward_batch + self.alpha * ll(
cat, s) / torch.max(
ll(cat,
s)) + mask_batch * self.gamma * (min_qf_next_target)
else:
next_q_value = reward_batch + self.alpha * ll(
cat, s) + mask_batch * self.gamma * (min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
#Update the proxy Qs of the DAC according to the
#Qdac, pidac, QTdac
with torch.no_grad():
next_state_action, _, _ = pidac.sample(next_state_batch)
qf1_next_target, qf2_next_target = QTdac(next_state_batch,
next_state_action)
cat = torch.cat((state_batch, action_batch), dim=-1)
s = tenco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
act, _, _ = pidac.sample(state_batch)
next_q_value = reward_batch - self.alpha1 * (
(ll(cat, s) - torch.min(ll(cat, s))) /
(torch.max(ll(cat, s)) - torch.min(ll(cat, s)))) * ll(
act, action_batch) + mask_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = Qdac(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(qf2, next_q_value)
Qdac_optim.zero_grad() #Update 2nd Agent's proxy networks
qf1_loss.backward()
Qdac_optim.step()
Qdac_optim.zero_grad()
qf2_loss.backward()
Qdac_optim.step()
#Find the value of F--the influence function
pi_BAC, _, _ = self.policy.sample(state_batch)
with torch.no_grad():
next_state_action, _, _ = pidac.sample(next_state_batch)
qf1_next_target, qf2_next_target = QTdac(next_state_batch,
next_state_action)
cat = torch.cat((state_batch, pi_BAC), dim=-1)
s = enco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
act, _, _ = pidac.sample(state_batch)
next_q_value = reward_batch - self.alpha1 * (
(ll(cat, s) - torch.min(ll(cat, s))) /
(torch.max(ll(cat, s)) - torch.min(ll(cat, s)))) * ll(
act,
pi_BAC) + mask_batch * self.gamma * (min_qf_next_target)
qf1, qf2 = Qdac(state_batch, pi_BAC)
qf1_loss = F.mse_loss(qf1, next_q_value)
qf2_loss = F.mse_loss(qf2, next_q_value)
minlossinf = torch.min(qf1_loss, qf2_loss)
qf1_pi, qf2_pi = self.critic(state_batch, pi_BAC)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = (-min_qf_pi).mean()
policy_loss += 0.1 * minlossinf #Regulate the objective function of the first agent by adding F
self.policy_optim.zero_grad()
policy_loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1)
self.policy_optim.step()
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self,
env_name,
enco,
suffix="",
actor_path=None,
critic_path=None,
enco_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/actor/IRLIA_actor_{}_{}".format(
env_name, suffix)
if critic_path is None:
critic_path = "models/critic/IRLIA_critic_{}_{}".format(
env_name, suffix)
if enco_path is None:
enco_path = "models/enco/IRLIA_enco_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
torch.save(enco.state_dict(), enco_path)
# Load model parameters
def load_model(self, enco, actor_path, critic_path, enco_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
if enco_path is not None:
enco.load_state_dict(torch.load(enco_path))
class SAC_fourier(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.Qapproximation = args.Qapproximation
try:
self.filter = args.filter
except:
self.filter = 'none'
try:
self.TDfilter = args.TDfilter
except:
self.TDfilter = 'none'
if args.Qapproximation == 'fourier':
self.critic = Qfourier(num_inputs,
action_space.shape[0],
256,
action_space,
gridsize=20).to(device=self.device)
# target doesn't need to filter Q in high frequencies
self.critic_target = Qfourier(num_inputs,
action_space.shape[0],
256,
action_space,
gridsize=20).to(device=self.device)
if args.Qapproximation == 'byactiondim':
self.critic = Qbyactiondim(num_inputs, action_space.shape[0], 256,
8, 5,
action_space).to(device=self.device)
self.critic_target = Qbyactiondim(
num_inputs, action_space.shape[0], 256, 8, 5,
action_space).to(device=self.device)
# self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
# self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
action, _, _, _ = self.policy.sample(state)
else:
_, _, action, _ = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
qf1, filterbw = self.critic(state_batch, action_batch)
with torch.no_grad():
next_state_action, next_state_log_pi, _, std = self.policy.sample(
next_state_batch)
if self.Qapproximation == 'byactiondim':
qf1_next_target = self.critic_target(next_state_batch,
next_state_action)
if self.Qapproximation == 'fourier' and self.TDfilter == 'none':
qf1_next_target = self.critic_target(next_state_batch,
next_state_action)
if self.Qapproximation == 'fourier' and self.TDfilter == 'rec_inside':
# with torch.no_grad():
# _, _, _, std = self.policy.sample(state_batch)
qf1_next_target, filterpower = self.critic_target(
next_state_batch,
next_state_action,
std=std, # detached
logprob=None,
mu=None,
filter=self.TDfilter,
filterbw=filterbw)
min_qf_next_target = qf1_next_target - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
# >>>> added filter regularization
qf1_loss = F.mse_loss(qf1, next_q_value) + 1 * torch.mean(
torch.pow(
torch.log(filterbw / (1 - filterbw)) # (-inf,inf)
,
2))
# >>>> added filter regularization
if self.Qapproximation == 'byactiondim':
pi, log_pi, _, _ = self.policy.sample(state_batch)
qf1_pi = self.critic(state_batch, pi)
if self.Qapproximation == 'fourier':
if self.filter == 'none':
# with torch.no_grad():
# _, _, _, std = self.policy.sample(state_batch)
pi, log_pi, _, std = self.policy.sample(state_batch)
qf1_pi, _ = self.critic(state_batch, pi)
if self.filter == 'rec_inside':
with torch.no_grad():
_, _, _, std = self.policy.sample(state_batch)
pi, log_pi, _, _ = self.policy.sample(state_batch)
qf1_pi, _ = self.critic(
state_batch,
pi,
std=std, # detached
logprob=None,
mu=None,
filter=self.filter,
cutoffXX=self.cutoffX)
if self.filter == 'rec_outside':
with torch.no_grad():
_, _, mu, std, log_pi_ = self.policy.sample_for_spectrogram(
state_batch)
pi, log_pi, _, _, _ = self.policy.sample_for_spectrogram(
state_batch)
qf1_pi, _ = self.critic(
state_batch,
pi,
std=std, # detached
logprob=log_pi_, # sum of logprobs w/o tanh correction
mu=mu,
filter=self.filter,
cutoffXX=self.cutoffX)
min_qf_pi = qf1_pi
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean()
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
filterbw0_tlogs = torch.mean(torch.tensor(filterbw),
0)[0] # For TensorboardX logs
filterbw1_tlogs = torch.mean(torch.tensor(filterbw),
0)[1] # For TensorboardX logs
filterbw2_tlogs = torch.mean(torch.tensor(filterbw),
0)[2] # For TensorboardX logs
filterpower_tlogs = torch.mean(
filterpower) # For TensorboardX logs
filterpower_tlogs = torch.mean(
filterpower) # For TensorboardX logs
min_qf_pi_tlogs = torch.mean(min_qf_pi) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
# print('{:5.2f} {:5.2f} {:5.2f}'.format(std[:,0].std().item(),std[:,1].std().item(),std[:,2].std().item()))
return qf1_loss.item(), 0, policy_loss.item(), alpha_loss.item(), alpha_tlogs.item(), \
std.mean().item(), \
filterbw0_tlogs.item(), filterbw1_tlogs.item(), filterbw2_tlogs.item(), \
filterpower_tlogs.item(), \
min_qf_pi_tlogs.item()
# Save model parameters
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "./models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "./models/sac_critic_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
def spectrum(self, memory, batch_size, action_space, To=2, modes=10):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch, \
log_prob_batch, std_batch = memory.sample(batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
std_batch = torch.FloatTensor(std_batch).to(self.device).squeeze(1)
log_prob_batch = torch.FloatTensor(log_prob_batch).to(
self.device).squeeze(1)
prob_batch = torch.exp(log_prob_batch)
with torch.no_grad():
qf1 = self.critic.spectrum(state_batch, action_batch, std_batch,
prob_batch, action_space, To, modes)
class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
# Q network, which yields a certain value for (a_t | s_t) pair
self.critic = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
# a sort of a replica - since, due to Bellman recursive definition, Q network learns from itself- and its unstbale
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device)
# the start point is same weights in both networks.
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# todo: crunch on this automatic alpha update
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
# instanciating of policy - given a state it produces probabilities for actions
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
# todo: what's difference between deterministic to Gaussian
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
action = action.detach().cpu().numpy()
return action[0]
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
# predict your own stuff - note that critic_target is never back-propagated
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
# Two Q-functions to mitigate positive bias in the policy improvement step
qf1, qf2 = self.critic(state_batch, action_batch)
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
actBatch, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, actBatch)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
# once in a few cycles of learning we update the target net
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
shuffle=True, # 要不要打乱数据 (打乱比较好)
)
action_space = spaces.Box(low=-np.array([0.5, 2.0]),
high=+np.array([0.5, 2.0]),
shape=(2, ),
dtype=np.float32)
net = DeterministicPolicy(6, 2, 256, action_space).to('cuda')
optimizer = torch.optim.SGD(net.parameters(), lr=0.05)
loss_func = torch.nn.MSELoss() # this is for regression mean squared loss
for epoch in range(1000): # 训练所有!整套!数据 3 次
for step, (batch_x,
batch_u) in enumerate(loader): # 每一步 loader 释放一小批数据用来学习
# print(batch_x.shape)
prediction = net(batch_x.cuda()) # input x and predict based on x
loss = loss_func(prediction,
batch_u.cuda()) # must be (1. nn output, 2. target)
optimizer.zero_grad() # clear gradients for next train
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if (step == 0):
prediction_eval = net(x_eval.cuda())
loss_eval = loss_func(prediction_eval, u_eval.cuda())
print('Epoch: ', epoch, '| Step: ', step, '| loss : ', loss_eval)
if (epoch % 100 == 0):
torch.save(net.state_dict(), "models/pretrain")
class BAC(object):
def __init__(self, num_inputs, action_space, args):
self.args = args
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
self.l = []
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, evaluate=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if evaluate is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_model1(self, model, new_params):
index = 0
for params in model.parameters():
params_length = len(params.view(-1))
new_param = new_params[index:index + params_length]
new_param = new_param.view(params.size())
params.data.copy_(new_param.to("cuda:0") + params.to("cuda:0"))
index += params_length
def update_parametersbefore(self, memory, batch_size, updates):
'''
Temporarily updates the parameters of the agent w.r.t. external rewards only.
Parameters
----------
memory : class 'replay_memory.ReplayMemory'
batch_size : int
updates : int
'''
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, _, _ = self.policy.sample(next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(qf2, next_q_value)
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = (-min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[− Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
def update_parametersafter(self, memory, batch_size, updates, env, enco):
'''
Updates the parameters of the agent w.r.t. external and intrinsic rewards.
Parameters
----------
memory : class 'replay_memory.ReplayMemory'
batch_size : int
updates : int
env : 'gym.wrappers.time_limit.TimeLimit'
The environment of interest
enco : class
The corresponding autoencoder
'''
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
cat = torch.cat((next_state_batch, next_state_action), dim=-1)
s = enco(cat)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target)
next_q_value = reward_batch + self.alpha * ll(cat, s) / torch.max(
ll(cat, s)) + mask_batch * self.gamma * (min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(qf1, next_q_value)
qf2_loss = F.mse_loss(qf2, next_q_value)
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = (-min_qf_pi).mean()
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
if updates % 3 == 0:
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self,
env_name,
enco,
suffix="",
actor_path=None,
critic_path=None,
enco_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/actor/sac_actor_{}_{}".format(
env_name, suffix)
if critic_path is None:
critic_path = "models/critic/sac_critic_{}_{}".format(
env_name, suffix)
if enco_path is None:
enco_path = "models/enco/sac_enco_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
torch.save(enco.state_dict(), enco_path)
# Load model parameters
def load_model(self, enco, actor_path, critic_path, enco_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
if enco_path is not None:
enco.load_state_dict(torch.load(enco_path))
class SAC_FORK(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.policy_type = args.policy_type
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
self.sysmodel = SysModel(num_inputs, action_space.shape[0],
args.sys_hidden_size,
args.sys_hidden_size).to(self.device)
self.sysmodel_optimizer = Adam(self.sysmodel.parameters(), lr=args.lr)
self.obs_upper_bound = 0 #state space upper bound
self.obs_lower_bound = 0 #state space lower bound
self.sysr = Sys_R(num_inputs, action_space.shape[0],
args.sysr_hidden_size,
args.sysr_hidden_size).to(self.device)
self.sysr_optimizer = torch.optim.Adam(self.sysr.parameters(),
lr=args.lr)
self.sys_threshold = args.sys_threshold
self.sys_weight = args.sys_weight
self.sysmodel_loss = 0
self.sysr_loss = 0
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, evaluate=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if evaluate is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf_loss = qf1_loss + qf2_loss
self.critic_optim.zero_grad()
qf_loss.backward()
self.critic_optim.step()
predict_next_state = self.sysmodel(state_batch, action_batch)
predict_next_state = predict_next_state.clamp(self.obs_lower_bound,
self.obs_upper_bound)
sysmodel_loss = F.smooth_l1_loss(predict_next_state,
next_state_batch.detach())
self.sysmodel_optimizer.zero_grad()
sysmodel_loss.backward()
self.sysmodel_optimizer.step()
self.sysmodel_loss = sysmodel_loss.item()
predict_reward = self.sysr(state_batch, next_state_batch, action_batch)
sysr_loss = F.mse_loss(predict_reward, reward_batch.detach())
self.sysr_optimizer.zero_grad()
sysr_loss.backward()
self.sysr_optimizer.step()
self.sysr_loss = sysr_loss.item()
s_flag = 1 if sysmodel_loss.item() < self.sys_threshold else 0
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
if s_flag == 1 and self.sys_weight != 0:
p_next_state = self.sysmodel(state_batch, pi)
p_next_state = p_next_state.clamp(self.obs_lower_bound,
self.obs_upper_bound)
p_next_r = self.sysr(state_batch, p_next_state.detach(), pi)
pi2, log_pi2, _ = self.policy.sample(p_next_state.detach())
p_next_state2 = self.sysmodel(p_next_state, pi2)
p_next_state2 = p_next_state2.clamp(self.obs_lower_bound,
self.obs_upper_bound)
p_next_r2 = self.sysr(p_next_state.detach(),
p_next_state2.detach(), pi2)
pi3, log_pi3, _ = self.policy.sample(p_next_state2.detach())
qf3_pi, qf4_pi = self.critic(p_next_state2.detach(), pi3)
min_qf_pi2 = torch.min(qf3_pi, qf4_pi)
#sys_loss = (-p_next_r -self.gamma * p_next_r2 + self.gamma ** 2 * ((self.alpha * log_pi3) - min_qf_pi2)).mean()
sys_loss = (-p_next_r + self.alpha * log_pi - self.gamma *
(p_next_r2 - self.alpha * log_pi2) + self.gamma**2 *
((self.alpha * log_pi3) - min_qf_pi2)).mean()
policy_loss += self.sys_weight * sys_loss
self.update_sys += 1
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save(self, filename):
torch.save(self.critic.state_dict(), filename + "_critic")
torch.save(self.critic_optim.state_dict(),
filename + "_critic_optimizer")
torch.save(self.policy.state_dict(), filename + "_actor")
torch.save(self.policy_optim.state_dict(),
filename + "_actor_optimizer")
torch.save(self.sysmodel.state_dict(), filename + "_sysmodel")
torch.save(self.sysmodel_optimizer.state_dict(),
filename + "_sysmodel_optimizer")
torch.save(self.sysr.state_dict(), filename + "_reward_model")
torch.save(self.sysr_optimizer.state_dict(),
filename + "_reward_model_optimizer")
def load(self, filename):
self.critic.load_state_dict(torch.load(filename + "_critic.pth"))
self.critic_optim.load_state_dict(
torch.load(filename + "_critic_optimizer"))
self.critic_target = copy.deepcopy(self.critic)
self.policy.load_state_dict(torch.load(filename + "_actor.pth"))
self.policy_optim.load_state_dict(
torch.load(filename + "_actor_optimizer"))
self.sysmodel.load_state_dict(torch.load(filename + "_sysmodel.pth"))
relf.sysmodel_optimizer.load_state_dict(
torch.load(filename + "_sysmodel_optimizer"))
self.sysr.load_state_dict(torch.load(filename + "_reward_model.pth"))
relf.sysr_optimizer.load_state_dict(
torch.load(filename + "_reward_model_optimizer"))
class ADVSAC(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.adv_epsilon = args.adv_epsilon
self.adv_lambda = args.adv_lambda
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)
# get adversarial perturbations for qf1 and qf2 respectively
state_batch.requires_grad = True
qf1, qf2 = self.critic_target(state_batch, action_batch)
adv_qf1_loss = qf1.mean()
self.critic_optim.zero_grad()
adv_qf1_loss.backward(retain_graph=True)
adv_perturb_1 = F.normalize(state_batch.grad.data)
state_batch.requires_grad = True
adv_qf2_loss = qf2.mean()
self.critic_optim.zero_grad()
adv_qf2_loss.backward(retain_graph=True)
adv_perturb_2 = F.normalize(state_batch.grad.data)
# get Q1 and Q2 adversarial estimation
adv_state_1 = state_batch - self.adv_epsilon * adv_perturb_1
adv_state_2 = state_batch - self.adv_epsilon * adv_perturb_2
adv_qf1, _ = self.critic_target(adv_state_1, action_batch)
_, adv_qf2 = self.critic_target(adv_state_2, action_batch)
adv_error_1 = torch.clamp(qf1 - adv_qf1, 0.0, 10000.0)
adv_error_2 = torch.clamp(qf1 - adv_qf2, 0.0, 10000.0)
next_q_value_1 = next_q_value - self.adv_lambda * adv_error_1
next_q_value_2 = next_q_value - self.adv_lambda * adv_error_2
qff1, qff2 = self.critic(state_batch, action_batch)
qf1_loss = F.mse_loss(qff1, next_q_value_1.detach())
qf2_loss = F.mse_loss(qff2, next_q_value_2.detach())
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item(), adv_error_1.mean().item(), adv_error_2.mean().item()
# Save model parameters
def save_model(self, env_name, suffix="", actor_path=None, critic_path=None):
if not os.path.exists('./train/'):
os.makedirs('./train/')
if not os.path.exists('./train/{}/'.format(env_name)):
os.makedirs('./train/{}/'.format(env_name))
if actor_path is None:
actor_path = "./train/{}/sac_actor_{}_{}.pth".format(env_name, env_name, suffix)
if critic_path is None:
critic_path = "./train/{}/sac_critic_{}_{}.pth".format(env_name, env_name, suffix)
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, env_name, seed):
actor_path = './train/{}/sac_actor_{}_{}.pth'.format(env_name, env_name, seed)
critic_path = './train/{}/sac_critic_{}_{}.pth'.format(env_name, env_name, seed)
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
class SAC(object):
def __init__(self,
num_inputs,
action_space,
args,
process_obs=None,
opt_level='O1'):
self.gamma = args.gamma
self.tau = args.tau
self.alpha = args.alpha
self.device = torch.device("cuda" if args.cuda else "cpu")
self.dtype = torch.float
self.policy_type = args.policy
self.target_update_interval = args.target_update_interval
self.automatic_entropy_tuning = args.automatic_entropy_tuning
self.process_obs = process_obs.to(self.device).to(self.dtype)
self.critic = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(device=self.device).to(
self.dtype)
self.critic_optim = Adam(list(self.critic.parameters()) +
list(process_obs.parameters()),
lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(self.device).to(
self.dtype)
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device,
dtype=self.dtype)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size, action_space).to(
self.device).to(self.dtype)
self.policy_optim = Adam(list(self.policy.parameters()) +
list(process_obs.parameters()),
lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(
num_inputs, action_space.shape[0], args.hidden_size,
action_space).to(self.device).to(self.dtype)
self.policy_optim = Adam(list(self.policy.parameters()) +
list(process_obs.parameters()),
lr=args.lr)
if opt_level is not None:
model, optimizer = amp.initialize([
self.policy, self.process_obs, self.critic, self.critic_target
], [self.policy_optim, self.critic_optim],
opt_level=opt_level)
def select_action(self, obs, evaluate=False):
with torch.no_grad():
obs = torch.FloatTensor(obs).to(self.device).unsqueeze(0).to(
self.dtype)
state = self.process_obs(obs)
if evaluate is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
action = action.detach().cpu().numpy()[0]
return action
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
obs_batch, action_batch, reward_batch, next_obs_batch, mask_batch = memory.sample(
batch_size=batch_size)
obs_batch = torch.FloatTensor(obs_batch).to(self.device).to(self.dtype)
next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device).to(
self.dtype)
action_batch = torch.FloatTensor(action_batch).to(self.device).to(
self.dtype)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1).to(self.dtype)
mask_batch = torch.FloatTensor(mask_batch).to(
self.device).unsqueeze(1).to(self.dtype)
state_batch = self.process_obs(obs_batch)
with torch.no_grad():
next_state_batch = self.process_obs(next_obs_batch)
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch)
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action)
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
qf_loss = qf1_loss + qf2_loss
self.critic_optim.zero_grad()
assert torch.isfinite(qf_loss).all()
with amp.scale_loss(qf_loss, self.critic_optim) as qf_loss:
qf_loss.backward()
self.critic_optim.step()
state_batch = self.process_obs(obs_batch)
pi, log_pi, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi = self.critic(state_batch.detach(), pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.policy_optim.zero_grad()
assert torch.isfinite(policy_loss).all()
with amp.scale_loss(policy_loss, self.policy_optim) as policy_loss:
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device).to(self.dtype)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self,
actor_path=None,
critic_path=None,
process_obs_path=None):
logger.debug(
f'saving models to {actor_path} and {critic_path} and {process_obs_path}'
)
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
torch.save(self.process_obs.state_dict(), process_obs_path)
# Load model parameters
def load_model(self,
actor_path=None,
critic_path=None,
process_obs_path=None):
logger.info(
f'Loading models from {actor_path} and {critic_path} and {process_obs_path}'
)
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
if process_obs_path is not None:
self.process_obs.load_state_dict(torch.load(process_obs_path))
class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma #γ
self.tau = args.tau #τ
self.alpha = args.alpha #α
self.policy_type = args.policy #策略类型，高斯随机策略、确定性策略
self.target_update_interval = args.target_update_interval #target network更新间隔
self.automatic_entropy_tuning = args.automatic_entropy_tuning #自动调熵
self.device = torch.device("cuda" if args.cuda else "cpu")
self.critic = QNetwork(num_inputs,
action_space.shape[0], args.hidden_size).to(
device=self.device) #Critic Network，Q网络
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
self.critic_target = QNetwork(num_inputs, action_space.shape[0],
args.hidden_size).to(
self.device) #Target Q Network
hard_update(self.critic_target, self.critic)
if self.policy_type == "Gaussian":
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning == True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space.shape).to(
self.device)).item() #torch.prod(input) : 返回所有元素的乘积
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
self.alpha = 0
self.automatic_entropy_tuning = False
self.policy = DeterministicPolicy(num_inputs,
action_space.shape[0],
args.hidden_size,
action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
def select_action(self, state, eval=False):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
if eval == False:
action, _, _ = self.policy.sample(
state) #action, log_prob, torch.tanh(mean)
else:
_, _, action = self.policy.sample(
state) #action, log_prob, torch.tanh(mean)
return action.detach().cpu().numpy()[0]
def update_parameters(self, memory, batch_size, updates):
# Sample a batch from memory
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(
batch_size=batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
next_state_batch) #下一个状态下采取的动作，下一个状态的log概率
qf1_next_target, qf2_next_target = self.critic_target(
next_state_batch, next_state_action) #Q1目标值和Q2目标值
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (
min_qf_next_target) #r(st,at) + γ(𝔼st+1~p[V(st+1)]))
qf1, qf2 = self.critic(
state_batch, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(
qf1, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2],下一状态的值是target network算出来的
qf2_loss = F.mse_loss(
qf2, next_q_value
) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
pi, log_pi, _ = self.policy.sample(state_batch) #动作，动作的对数概率
qf1_pi, qf2_pi = self.critic(state_batch, pi) #动作的Q值
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean(
) # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()
).mean() #E[-αlogπ(at|st)-αH]
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0: #每隔几步更新target network
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
# Save model parameters
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Load model parameters
def load_model(self, actor_path, critic_path):
print('Loading models from {} and {}'.format(actor_path, critic_path))
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))