def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
class soft_actor_critic_agent(object):
def __init__(self, num_inputs, action_space, \
device, hidden_size, seed, lr, gamma, tau, alpha):
self.gamma = gamma
self.tau = tau
self.alpha = alpha
self.device = device
self.seed = seed
self.seed = torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
#torch.cuda.manual_seed_all(seed)
#torch.backends.cudnn.deterministic=True
self.critic = QNetwork(seed, num_inputs, action_space.shape[0], hidden_size).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=lr)
self.critic_target = QNetwork(seed, num_inputs, action_space.shape[0], hidden_size).to(self.device)
hard_update(self.critic_target, self.critic)
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
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=lr)
self.policy = GaussianPolicy(seed, num_inputs, action_space.shape[0], \
hidden_size, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=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)
Q1_next_target, Q2_next_target = self.critic_target(next_state_batch, next_state_action)
min_q_next_target = torch.min(Q1_next_target, Q2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + mask_batch * self.gamma * (min_q_next_target)
# Two Q-functions to mitigate positive bias in the policy improvement step
Q1, Q2 = self.critic(state_batch, action_batch)
Q1_loss = F.mse_loss(Q1, next_q_value)
Q2_loss = F.mse_loss(Q2, next_q_value)
action_batch_pi, log_pi, _ = self.policy.sample(state_batch)
Q1_pi, Q2_pi = self.critic(state_batch, action_batch_pi)
min_q_pi = torch.min(Q1_pi, Q2_pi)
policy_loss = ((self.alpha * log_pi) - min_q_pi).mean()
self.critic_optim.zero_grad()
Q1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
Q2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
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
soft_update(self.critic_target, self.critic, self.tau)
class AbstractAgent(metaclass=ABCMeta):
"""Abstract Base Agent"""
def __init__(self, state_size, action_size, memory, seed, configs):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.lr = configs['LR']
self.update_every = configs['UPDATE_EVERY']
self.batch_size = configs['BATCH_SIZE']
self.gamma = configs['GAMMA']
self.tau = configs['TAU']
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=self.lr)
# Replay memory
# ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
self.memory = memory
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
# Loss
self.criterion = nn.MSELoss()
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self._learn(experiences, self.gamma)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
@abstractmethod
def _learn(self, experiences, gamma):
raise NotImplementedError
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")
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 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]
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,
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))
class SAC(object):
def __init__(self, input_space, action_space, args):
self.use_expert = args.use_expert
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.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print(torch.cuda.is_available())
# print(torch.cuda.current_device())
# print(torch.cuda.device(0))
# print(torch.cuda.device_count())
# print(torch.cuda.get_device_name())
# print(torch.backends.cudnn.version())
# print(torch.backends.cudnn.is_available())
self.critic = QNetwork(input_space, 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(input_space, 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 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(input_space, action_space.shape[0], args.hidden_size, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
else:
raise ValueError("Not supper another type yet.")
# SAC_V
# self.value = ValueNetwork(input_space).to(device=self.device)
# self.value_target = ValueNetwork(input_space).to(self.device)
# self.value_optim = Adam(self.value.parameters(), lr=args.lr)
# hard_update(self.value_target, self.value)
# self.policy = GaussianPolicy(input_space, 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_map = torch.FloatTensor(state['map']).to(self.device).unsqueeze(0)
state_lidar = torch.FloatTensor(state['lidar']).to(self.device).unsqueeze(0)
state_goal = torch.FloatTensor(state['goal']).to(self.device).unsqueeze(0)
state_plan_len = torch.FloatTensor(state['plan_len']).to(self.device).unsqueeze(0)
state_robot_info= torch.FloatTensor(state['robot_info']).to(self.device).unsqueeze(0)
state = {'map': state_map, 'lidar': state_lidar, 'goal': state_goal, 'plan_len': state_plan_len, 'robot_info': state_robot_info}
if eval is False:
action, _, _ = self.policy.sample(state)
else:
_, _, action = self.policy.sample(state)
action = action.detach().cpu().numpy()[0]
# return self.rescale_action(action)
return 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
if not self.use_expert:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)
else:
state_batch, action_batch, reward_batch, next_state_batch, mask_batch, s_e_batch, a_e_batch = memory.sample(batch_size=batch_size, use_expert=True)
# State is array of dictionary like [{"map":value, "lidar":value, "goal":value}, ...]
# So, convert list to dict below:
_state_batch = {'map':[], 'lidar':[], 'goal':[], 'plan_len':[], 'robot_info':[]}
_next_state_batch = {'map':[], 'lidar':[], 'goal':[], 'plan_len':[], 'robot_info':[]}
_state_expert_batch = {'map':[], 'lidar':[], 'goal':[], 'plan_len':[], 'robot_info':[]}
for s in state_batch:
_state_batch['map'].append(s['map'])
_state_batch['lidar'].append(s['lidar'])
_state_batch['goal'].append(s['goal'])
_state_batch['plan_len'].append(s['plan_len'])
_state_batch['robot_info'].append(s['robot_info'])
for s in next_state_batch:
_next_state_batch['map'].append(s['map'])
_next_state_batch['lidar'].append(s['lidar'])
_next_state_batch['goal'].append(s['goal'])
_next_state_batch['plan_len'].append(s['plan_len'])
_next_state_batch['robot_info'].append(s['robot_info'])
if self.use_expert:
for s in s_e_batch:
_state_expert_batch['map'].append(s['map'])
_state_expert_batch['lidar'].append(s['lidar'])
_state_expert_batch['goal'].append(s['goal'])
_state_expert_batch['plan_len'].append(s['plan_len'])
_state_expert_batch['robot_info'].append(s['robot_info'])
_state_batch['map'] = torch.FloatTensor(_state_batch['map']).to(self.device)
_state_batch['lidar'] = torch.FloatTensor(_state_batch['lidar']).to(self.device)
_state_batch['goal'] = torch.FloatTensor(_state_batch['goal']).to(self.device)
_state_batch['plan_len'] = torch.FloatTensor(_state_batch['plan_len']).to(self.device)
_state_batch['robot_info'] = torch.FloatTensor(_state_batch['robot_info']).to(self.device)
_next_state_batch['map'] = torch.FloatTensor(_next_state_batch['map']).to(self.device)
_next_state_batch['lidar'] = torch.FloatTensor(_next_state_batch['lidar']).to(self.device)
_next_state_batch['goal'] = torch.FloatTensor(_next_state_batch['goal']).to(self.device)
_next_state_batch['plan_len'] = torch.FloatTensor(_next_state_batch['plan_len']).to(self.device)
_next_state_batch['robot_info'] = torch.FloatTensor(_next_state_batch['robot_info']).to(self.device)
if self.use_expert:
_state_expert_batch['map'] = torch.FloatTensor(_state_expert_batch['map']).to(self.device)
_state_expert_batch['lidar'] = torch.FloatTensor(_state_expert_batch['lidar']).to(self.device)
_state_expert_batch['goal'] = torch.FloatTensor(_state_expert_batch['goal']).to(self.device)
_state_expert_batch['plan_len'] = torch.FloatTensor(_state_expert_batch['plan_len']).to(self.device)
_state_expert_batch['robot_info'] = torch.FloatTensor(_state_expert_batch['robot_info']).to(self.device)
_action_expert_batch = torch.FloatTensor(a_e_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)
# SAC_V
# with torch.no_grad():
# vf_next_target = self.value_target(_new_next_state_batch)
# next_q_value = reward_batch + mask_batch * self.gamma * (vf_next_target)
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()
# Update Policy
if not self.use_expert:
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)
else:
pi, log_pi, _ = self.policy.sample(_state_expert_batch)
qf1_pi, qf2_pi = self.critic(_state_expert_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.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()
# # SAC_V
# # Regularization Loss
# reg_loss = 0.001 * (mean.pow(2).mean() + log_std.pow(2).mean())
# policy_loss += reg_loss
# self.policy_optim.zero_grad()
# policy_loss.backward()
# self.policy_optim.step()
# # Update Value
# if not self.use_expert:
# vf = self.value(_new_state_batch)
# else:
# vf = self.value(_new_s_e_batch)
# with torch.no_grad():
# vf_target = min_qf_pi - (self.alpha * log_pi)
# vf_loss = F.mse_loss(vf, vf_target) # JV = 𝔼(st)~D[0.5(V(st) - (𝔼at~π[Q(st,at) - α * logπ(at|st)]))^2]
# self.value_optim.zero_grad()
# vf_loss.backward()
# self.value_optim.step()
# if updates % self.target_update_interval == 0:
# soft_update(self.value_target, self.value, self.tau)
# return vf_loss.item(), qf1_loss.item(), qf2_loss.item(), policy_loss.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\n {}\n, {}\n'.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\n {}\n, {}\n'.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 Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, compute_weights=False):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.compute_weights = compute_weights
# Algorithms to enable during training
self.PrioritizedReplayBuffer = True # Use False to enable uniform sampling
self.HardTargetUpdate = True # Use False to enable soft target update
# building the policy and target Q-networks for the agent, such that the target Q-network is kept frozen to avoid the training instability issues
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device) # main policy network
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device) # target network
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.criterion = nn.MSELoss()
# Replay memory
# building the experience replay memory used to avoid training instability issues
# Below: PER
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
EXPERIENCES_PER_SAMPLING, seed,
compute_weights)
# Below: Uniform by method defined in this script
#self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_NN_EVERY steps)
self.t_step_nn = 0
# Initialize time step (for updating every UPDATE_MEM_PAR_EVERY steps)
self.t_step_mem_par = 0
# Initialize time step (for updating every UPDATE_MEM_EVERY steps)
self.t_step_mem = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_NN_EVERY time steps.
self.t_step_nn = (self.t_step_nn + 1) % UPDATE_NN_EVERY
self.t_step_mem = (self.t_step_mem + 1) % UPDATE_MEM_EVERY
self.t_step_mem_par = (self.t_step_mem_par + 1) % UPDATE_MEM_PAR_EVERY
if self.t_step_mem_par == 0:
self.memory.update_parameters()
if self.t_step_nn == 0:
# If enough samples are available in memory, get random subset and learn
if self.memory.experience_count > EXPERIENCES_PER_SAMPLING:
sampling = self.memory.sample()
self.learn(sampling, GAMMA)
if self.t_step_mem == 0:
self.memory.update_memory_sampling()
def act(self, state, eps=0.):
"""A function to select an action based on the Epsilon greedy policy. Epislon percent of times the agent will select a random
action while 1-Epsilon percent of the time the agent will select the action with the highest Q value as predicted by the
neural network.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
# here we calculate action values (Q values)
self.qnetwork_local.eval(
) # model deactivate norm, dropout etc. layers as it is expected
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train(
) # model.train() sets the modules in the network in training mode
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.cpu().numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, sampling, gamma):
"""Update value parameters using given batch of experience tuples.
Function for training the neural network. The function will update the weights of the newtwork
Params
======
sampling (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones, weights, indices = sampling
# Target (absolute) Q values from target Q network
q_target = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Predictions from local Q network
expected_values = rewards + gamma * q_target * (1 - dones)
output = self.qnetwork_local(states).gather(1, actions)
# computing the loss
loss = F.mse_loss(output,
expected_values) # Loss Function: Mean Square Error
if self.compute_weights:
with torch.no_grad():
weight = sum(np.multiply(weights, loss.data.cpu().numpy()))
loss *= weight
# Minimizing the loss by optimizer
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
# ------------------- update priorities ------------------- #
delta = abs(expected_values - output.detach()).cpu().numpy()
#print("delta", delta)
self.memory.update_priorities(delta, indices)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
# def hard_update(self):
# """ This hard_update method performs direct update of target network
# weight update from local network weights instantly"""
# Write the algorithm here
def load_models(self, policy_net_filename, target_net_filename):
""" Function to load the parameters of the policy and target models """
print('Loading model...')
self.qnetwork_local.load_model(policy_net_filename)
self.qnetwork_target.load_model(target_net_filename)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# use helper calc_loss function & not this directly
self.criterion = nn.MSELoss()
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
# first unsqueeze to make it a batch with one sample in it
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
# set the mode back to training mode
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
# output of model needs to be Q values of actions 0 - (n-1) And output[ind_x] needs to correspond to action_x
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
# helper for learn func
# y_j does not depend on the weight parameters that gradient descent will be training
def calc_y_j(self, r_j, dones, gamma, target_out):
# 1 or 0 flag; if episode doesn't terminate at j+1 (aka done == False), y_j product now already includes the gamma multiplication factor
# use [[x] for x in y] kind of list comprehension because need them to be batch_size by 1 like r_j
# use .to(device) to move to gpu (not just setting device arg when creating a tensor)
dones_flags = torch.Tensor([[0] if done == True else [gamma] for done in dones]).float().to(device)
max_q_target_out = torch.Tensor([[torch.max(q_for_all_actions)] for q_for_all_actions in target_out]).float().to(device)
# RuntimeError: Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead.
#dones_flags = torch.from_numpy(np.vstack([0 if done == True else gamma for done in dones])).float().to(device)
#max_q_target_out = torch.from_numpy(np.vstack([torch.max(q_for_all_actions) for q_for_all_actions in target_out])).float().to(device)
y_j = r_j + dones_flags * max_q_target_out
return y_j
# helper for learn func
def calc_loss(self, y_j, pred_out, actions):
# need pred_out_actions_taken to be a tensor & built by combining (concatenating) other tensors to maintain gradients
# actions is batch_size by 1- only have to iterate through rows
for i in range(actions.shape[0]):
# action taken. is an index for which col to look at in pred_out (pred_out is batch_size by n_actions]
action_ind = actions[i, 0].item()
if i == 0:
# need to take h from 0 dimensional to 2 dimensional
pred_out_actions_taken = pred_out[i, action_ind].unsqueeze(0).unsqueeze(0)
else:
# concat along dim 0 -> vertically stack rows
pred_out_actions_taken = torch.cat((pred_out_actions_taken, pred_out[i, action_ind].unsqueeze(0).unsqueeze(0)), dim=0)
# loss is MSE between pred_out_actions_taken (input) and y_j (target)
return self.criterion(pred_out_actions_taken, y_j)
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
# torch
states, actions, rewards, next_states, dones = experiences
"*** YOUR CODE HERE ***"
## TODO: compute and minimize the loss
# vstack takes one argument (sequence)- stacks the pieces of that argument vertically
# SELF NOTE: think about what (if anything additional) needs .todevice()
# make sure to zero the gradients
self.optimizer.zero_grad()
# q_network_local model output from forward pass
pred_out = self.qnetwork_local(states)
target_out = self.qnetwork_target(next_states)
# compute the loss for q_network_local vs q_network_target
y_j = self.calc_y_j(rewards, dones, gamma, target_out)
# calc gradient & take step down the gradient
loss = self.calc_loss(y_j, pred_out, actions)
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
# save sample (error,<s,a,r,s'>) to the replay memory
def _append_sample(self, state, action, reward, next_state, done):
state_on_device = torch.from_numpy(state).float().unsqueeze(0).to(
device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state_on_device)
expected_Q = action_values[(0, action)]
self.qnetwork_local.train()
next_state_on_device = torch.from_numpy(next_state).float().unsqueeze(
0).to(device)
self.qnetwork_target.eval()
with torch.no_grad():
predicted_Q = self.qnetwork_target(next_state_on_device)
max_predicted_Q = predicted_Q.max(1)[0]
self.qnetwork_target.train()
target_Q = reward + (GAMMA * max_predicted_Q * (1 - done))
error = expected_Q - target_Q
error = abs(error.cpu().data.numpy()[0])
self.memory.add(state, action, reward, next_state, done)
# Simplified Prioritized Experience Replay
# add additional samples if the error is large
if error > 1.:
error = pow(error, 0.6)
count = int(round(error, 0))
for _ in range(count):
self.memory.add(state, action, reward, next_state, done)
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
if USE_SPER:
self._append_sample(state, action, reward, next_state, done)
else:
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# get the expected Q value using the local model
expected_Q = self.qnetwork_local(states).gather(1, actions)
if USE_DDQN:
# ----- DDQN
local_next_state_max_action = self.qnetwork_local(
next_states).detach().argmax(1).unsqueeze(1)
max_predicted_Q = self.qnetwork_target(
next_states).detach().gather(1, local_next_state_max_action)
else:
# ---- Vanilla DQN
max_predicted_Q = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# get the target Q using current state
target_Q = rewards + (gamma * max_predicted_Q * (1 - dones))
# get the loss
loss = F.mse_loss(expected_Q, target_Q)
# minimise the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent:
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, buffer_size=int(1e5), batch_size=64, gamma=0.99, tau=1e-3 , lr=5e-4, double_dqn=True, huber_loss=False, update_every=4):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.double_dqn = double_dqn
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
self.batch_size = batch_size
self.gamma = gamma
self.tau = tau
self.update_every = update_every
if huber_loss:
self.loss_function = F.smooth_l1_loss
else:
self.loss_function = F.mse_loss
# Replay memory
self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
"""
states, actions, rewards, next_states, dones = experiences
if self.double_dqn:
# Get predicted Q values from target model using the arg_max predicted by a local model
argmax_actions = self.qnetwork_local(next_states).detach().argmax(1).unsqueeze(1)
Q_targets_next = self.qnetwork_target(next_states).detach().gather(1, argmax_actions)
else:
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (self.gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = self.loss_function(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target)
def soft_update(self, local_model, target_model):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(self.tau*local_param.data + (1.0-self.tau)*target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy()) #expliding
else:
return random.choice(np.arange(self.action_size)) #exploration
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
"""
for i in range(BATCH_SIZE):
if not dones[i]:
max_val = self.qnetwork_target(next_states[i])
best_val = max_val.argmax()
target = rewards[i] + gamma*(max_val[best_val])
else:
target = rewards[i]
current = self.qnetwork_local(states[i])[actions[i]]
#current = self.qnetwork_local(states).gather(-1, actions.reshape(actions.size()[0], 1))
self.loss = F.mse_loss(target, current)
#self.loss.requires_grad = True
self.optimizer.zero_grad()
self.loss.backward()
self.optimizer.step()
"""
max_val = self.qnetwork_target(next_states)
best_val = max_val.argmax(dim=-1)
max_val = max_val.gather(-1, best_val.reshape(max_val.size()[0], 1))
target = rewards + gamma * max_val * (1 - dones)
current = self.qnetwork_local(states).gather(
-1, actions.reshape(actions.size()[0], 1))
self.loss = F.mse_loss(current, target)
self.optimizer.zero_grad()
self.loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""
Initialize Agent, inclduing:
DQN Hyperparameters
Local and Targat State-Action Policy Networks
Replay Memory Buffer from Replay Buffer Class (define below)
"""
def __init__(self, state_size, action_size, dqn_type='DQN', replay_memory_size=1e5, batch_size=64, gamma=0.99,
learning_rate=1e-3, target_tau=2e-3, update_rate=4, seed=0):
"""
DQN Agent Parameters
======
state_size (int): dimension of each state
action_size (int): dimension of each action
dqn_type (string): can be either 'DQN' for vanillia dqn learning (default) or 'DDQN' for double-DQN.
replay_memory size (int): size of the replay memory buffer (typically 5e4 to 5e6)
batch_size (int): size of the memory batch used for model updates (typically 32, 64 or 128)
gamma (float): paramete for setting the discoun ted value of future rewards (typically .95 to .995)
learning_rate (float): specifies the rate of model learing (typically 1e-4 to 1e-3))
seed (int): random seed for initializing training point.
"""
self.dqn_type = dqn_type
self.state_size = state_size
self.action_size = action_size
self.buffer_size = int(replay_memory_size)
self.batch_size = batch_size
self.gamma = gamma
self.learn_rate = learning_rate
self.tau = target_tau
self.update_rate = update_rate
self.seed = random.seed(seed)
"""
# DQN Agent Q-Network
# For DQN training, two nerual network models are employed;
# (a) A network that is updated every (step % update_rate == 0)
# (b) A target network, with weights updated to equal the network at a slower (target_tau) rate.
# The slower modulation of the target network weights operates to stablize learning.
"""
self.network = QNetwork(state_size, action_size, seed).to(device)
self.target_network = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.network.parameters(), lr=self.learn_rate)
# Replay memory
self.memory = ReplayBuffer(action_size, self.buffer_size, self.batch_size, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
########################################################
# STEP() method
#
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % self.update_rate
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
########################################################
# ACT() method
#
def act(self, state, eps=0.0):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.network.eval()
with torch.no_grad():
action_values = self.network(state)
self.network.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
########################################################
# LEARN() method
# Update value parameters using given batch of experience tuples.
def learn(self, experiences, gamma, DQN=True):
"""
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get Q values from current observations (s, a) using model nextwork
Qsa = self.network(states).gather(1, actions)
if (self.dqn_type == 'DDQN'):
#Double DQN
#************************
Qsa_prime_actions = self.network(next_states).detach().max(1)[1].unsqueeze(1)
Qsa_prime_targets = self.target_network(next_states)[Qsa_prime_actions].unsqueeze(1)
else:
#Regular (Vanilla) DQN
#************************
# Get max Q values for (s',a') from target model
Qsa_prime_target_values = self.target_network(next_states).detach()
Qsa_prime_targets = Qsa_prime_target_values.max(1)[0].unsqueeze(1)
# Compute Q targets for current states
Qsa_targets = rewards + (gamma * Qsa_prime_targets * (1 - dones))
# Compute loss (error)
loss = F.mse_loss(Qsa, Qsa_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.network, self.target_network, self.tau)
########################################################
"""
Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
"""
def soft_update(self, local_model, target_model, tau):
"""
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
class Agent():
""" Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
""" Initialize an Agent object.
INPUTS:
------------
state_size - (int) dimension of each state
action_size - (int) dimension of each action
seed - (int) random seed
OUTPUTS:
------------
no direct
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
""" Update the agent's knowledge, using the most recently sampled tuple.
INPUTS:
------------
state - (array_like) the previous state of the environment (8,)
action - (int) the agent's previous choice of action
reward - (float) last reward received
next_state - (torch tensor) the current state of the environment
done - (bool) whether the episode is complete (True or False)
OUTPUTS:
------------
no direct
"""
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
#print('experiences')
#print(experiences)
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
""" Returns actions for given state as per current policy.
INPUTS:
------------
state - (numpy array_like) current state
eps - (float) epsilon, for epsilon-greedy action selection
OUTPUTS:
------------
act_select - (int) next epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
act_select = np.argmax(action_values.cpu().data.numpy())
return act_select
else:
act_select = random.choice(np.arange(self.action_size))
return act_select
def learn(self, experiences, gamma):
""" Update value parameters using given batch of experience tuples.
INPUTS:
------------
experiences - (Tuple[torch.Variable]) tuple of (s, a, r, s', done) tuples
gamma - (float) discount factor
OUTPUTS:
------------
"""
states, actions, rewards, next_states, dones = experiences
## Compute and minimize the loss
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
#print(Q_targets_next)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
#print(Q_targets)
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
#print(Q_expected)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
""" Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
INPUTS:
------------
local_model - (PyTorch model) weights will be copied from
target_model - (PyTorch model) weights will be copied to
tau - (float) interpolation parameter
OUTPUTS:
------------
no direct
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
def __init__(self, state_size, action_size, random_seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, random_seed)
self.qnetwork_target = QNetwork(state_size, action_size, random_seed)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Move to GPU if CUDA is available
if train_on_gpu:
self.qnetwork_local.cuda()
self.qnetwork_target.cuda()
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience / reward
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# Learn, if enough samples are available in memory
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().unsqueeze(0)
if train_on_gpu:
state = state.cuda()
self.qnetwork_local.eval()
action_values = self.qnetwork_local(Variable(state, volatile=True))
self.qnetwork_local.train()
max_action = np.argmax(action_values.cpu().data.numpy())
policy_s = np.ones(self.action_size) * eps / self.action_size
policy_s[max_action] = 1 - eps + (eps / self.action_size)
action = np.random.choice(np.arange(self.action_size), p=policy_s)
return action
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute loss
Q_expected = self.qnetwork_local(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ----------------------- update target network ----------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Double_DQNAgent(object):
"""Interacts with and learns from the environment."""
def __init__(self, args, state_size, action_size):
"""Initialize an Agent object.
Args:
param1 (args) : command line arguments
param2 (int) : state size environment
param3 (int) : dimension of each action
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(args.seed)
self.batch_size = args.batch_size
self.tau = args.tau
self.update_every = args.update_every
self.memory_size = args.buffer_size
self.gamma = args.discount
self.lr = args.lr
self.device = args.device
# Q-Network
if torch.cuda.is_available():
self.qnetwork_local = QNetwork(state_size, action_size,
args.hidden_size_1,
args.hidden_size_2, args.seed)
self.qnetwork_target = QNetwork(state_size, action_size,
args.hidden_size_1,
args.hidden_size_2, args.seed)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=self.lr)
# Replay memory
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def act_e_greedy(self, state, epsilon=0.001):
""" acts with epsilon greedy policy
epsilon exploration vs exploitation traide off
Args:
param1(int): state
param2(float): epsilon
Return : action int number between 0 and 4
"""
return random.choice(np.arange(
self.action_size)) if np.random.random() < epsilon else self.act(
state)
def act(self, state):
"""
acts greedy(max) based on a single state
Args:
param1 (int) : state
"""
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
return np.argmax(action_values.cpu().data.numpy())
def learn(self, mem):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
idxs, states, actions, rewards, next_states, nonterminals, weights = mem.sample(
self.batch_size)
states = states.squeeze(1)
q_values = self.qnetwork_local(states)
next_q_values = self.qnetwork_target(next_states)
next_q_values = next_q_values.squeeze(1)
q_value = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
next_q_value = next_q_values.max(1)[0]
nonterminals = nonterminals.squeeze(1)
expected_q_value = rewards + (self.gamma * next_q_value * nonterminals)
loss = F.mse_loss(q_value, expected_q_value)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
loss = (q_value - expected_q_value.detach()).pow(2) * weights
prios = loss + 1e-5
mem.update_priorities(idxs,
prios.detach().cpu().numpy()
) # Update priorities of sampled transitions
# ------------------- update target network ------------------- #
self.soft_update()
def soft_update(self, tau=1e-3):
""" swaps the network weights from the online to the target
Args:
param1 (float): tau
"""
for target_param, local_param in zip(self.qnetwork_target.parameters(),
self.qnetwork_local.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def update_target_net(self):
""" copy the model weights from the online to the target network """
self.qnetwork_target.load_state_dict(self.qnetwork_local.state_dict())
def save(self, path):
""" save the model weights to a file
Args:
param1 (string): pathname
"""
torch.save(self.qnetwork_local.state_dict(),
os.path.join(path, 'model.pth'))
def train(self):
""" activates the backprob. layers for the online network """
self.qnetwork_local.train()
def eval(self):
""" invoke the eval from the online network
deactivates the backprob
layers like dropout will work in eval model instead
"""
self.qnetwork_local.eval()
class Agent():
def __init__(self,
state_size,
action_size,
behavior_name,
index_player,
replay_memory_size=1e4,
batch_size=100,
gamma=0.99,
learning_rate=1e-4,
target_tau=1e-3,
update_rate=10,
seed=0):
self.state_size = state_size
self.current_state = []
self.action_size = action_size
self.buffer_size = int(replay_memory_size)
self.batch_size = batch_size
self.gamma = gamma
self.learn_rate = learning_rate
self.tau = target_tau
self.update_rate = update_rate
self.seed = random.seed(seed)
self.behavior_name = behavior_name
self.index_player = index_player
self.close_ball_reward = 0
self.touch_ball_reward = 0
"""
Now we define two models:
(a) one netwoek will be updated every (step % update_rate == 0),
(b) A target network, with weights updated to equal to equal to the network (a) at a slower (target_tau) rate.
"""
self.network = QNetwork(state_size, action_size, seed).to(device)
self.target_network = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.network.parameters(),
lr=self.learn_rate)
# Replay memory
self.memory = ReplayBuffer(action_size, self.buffer_size,
self.batch_size, seed)
# Initialize time step ( for updating every UPDATE_EVERY steps)
self.t_step = 0
def load_model(self, path_model, path_target=None):
params = torch.load(path_model)
#self.network.set_params(params)
self.network.load_state_dict(torch.load(path_model))
if path_target != None:
self.target_network.load_state_dict(torch.load(path_target))
def choose_action(self, state, eps=0.0):
eps = -1
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.network.eval()
with torch.no_grad():
action_values = self.network(state)
self.network.train()
# epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy()
) # return a number from 0 to action_size
else:
return random.choice(np.arange(
self.action_size)) # return a number from 0 to action_size
def Read(self):
decision_steps, terminal_steps = env.get_steps(self.behavior_name)
try:
signal_front = np.array(
sensor_front_sig(
decision_steps.obs[0][self.index_player, :])) # 3 x 11 x 8
signal_back = np.array(
sensor_back_sig(
decision_steps.obs[1][self.index_player, :])) # 3 x 3 x 8
#pre_state = []
signal_front = np.array(signal_front)
#print(signal_front.shape)
#print(signal_back.shape)
r = np.concatenate((signal_front, signal_back), axis=1)
#print(r.shape)
#input('ff')
#pre_state.extend(list(np.array(signal_front).flatten()))
#pre_state.extend(list(np.array(signal_back).flatten()))
#state = np.array(pre_state)
self.current_state = r
count_close_to_ball = 0
count_touch_ball = 0
count_back_touch = 0
count_back_close = 0
self.rew_d_to_our_post = 0
self.rew_for_ball_dist = -0.1
# Front Observation
for i in range(len(signal_front[0])):
if signal_front[0][i][0] == 1.0:
count_close_to_ball += 1
self.rew_for_ball_dist = max(
0.3 * (1 - signal_front[0][i][7]),
self.rew_for_ball_dist)
# Kicked the ball at the front
if signal_front[0][i][7] <= 0.03:
count_touch_ball += 1
if signal_front[0][i][1] == 1.0:
self.rew_d_to_our_post = -0.1
if signal_front[0][i][2] == 1.0:
self.rew_d_to_our_post = 0.1
# Back observation
for i in range(len(signal_back[0])):
if signal_back[0][i][0] == 1.0:
count_back_close += 1
# Touches the ball at the back
if signal_back[0][i][7] <= 0.03:
count_back_touch += 0
self.back_touch = 1 if count_back_touch > 0 else 0
self.back_close = 1 if count_back_close > 0 else 0
# add reward if kick the ball
self.touch_ball_reward = 3 if count_touch_ball > 0 else 0
# Penalize for back touching the ball
if count_back_touch > 0:
self.touch_ball_reward = -1.5
# Penalize if the ball is not in view
self.close_ball_reward = 0.1 if count_close_to_ball > 0 else -0.1
# Penalize if the ball is behind the agent
if count_back_close > 0:
self.close_ball_reward = -0.1
return self.current_state
except:
self.touch_ball_reward = 0
self.close_ball_reward = 0
return self.current_state
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
seed, ALPHA)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
# Initialize learning step for updating beta
self.learn_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get prioritized subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA, BETA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma, beta):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
beta (float): initial value for beta, which controls how much importance weights affect learning
"""
states, actions, rewards, next_states, dones, probabilities, indices = experiences
if double_dqn:
# Get the Q values for each next_state, action pair from the
# local/online/behavior Q network:
Q_targets_next_local = self.qnetwork_local(
next_states).detach()
# Get the corresponding best action for those next_states:
_, a_prime = Q_targets_next_local.max(1)
# Get the Q values from the target Q network but following a_prime,
# which belongs to the local network, not the target network:
Q_targets_next = self.qnetwork_target(next_states).detach()
Q_targets_next = Q_targets_next.gather(1, a_prime.unsqueeze(1))
else:
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(
next_states).detach().max(1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute and update new priorities
new_priorities = (abs(Q_expected - Q_targets) +
EPSILON_PER).detach()
self.memory.update_priority(new_priorities, indices)
# Update beta parameter (b). By default beta will reach 1 after
# 25,000 training steps (~325 episodes in the Banana environment):
b = min(1.0, beta + self.learn_step * (1.0 - beta) / BETA_ITERS)
self.learn_step += 1
# Compute and apply importance sampling weights to TD Errors
ISweights = (((1 / len(self.memory)) * (1 / probabilities))**b)
max_ISweight = torch.max(ISweights)
ISweights /= max_ISweight
Q_targets *= ISweights
Q_expected *= ISweights
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def save_model(model: QNetwork, output_file: Path) -> None:
"""Save the weights of the trained agent"""
torch.save(model.state_dict(), output_file)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed, 64,
64).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed, 64,
64).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.loss_fn = torch.nn.MSELoss()
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
# torch.nn.utils.clip_grad_value_(self.qnetwork_local.parameters(), clip_value = 1)
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
# # Get max predicted Q values (for next states) from target model
# Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# # Compute Q targets for current states
# Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# # Get expected Q values from local model
# Q_expected = self.qnetwork_local(states).gather(1, actions)
# # Compute loss
# loss = F.mse_loss(Q_expected, Q_targets)
# # Minimize the loss
# self.optimizer.zero_grad()
# loss.backward()
# self.optimizer.step()
optimizer = self.optimizer
loss_fn = self.loss_fn
## this is required as we're not learning qnetwork_targets weights
# with torch.no_grad():
# Q_target = rewards + gamma * (torch.max(self.qnetwork_target(next_states), dim=1)[0].view(64,1))*(1 - dones)
# Q_target[dones == True] = rewards[dones == True]
# Q_pred = torch.max(self.qnetwork_local(states), dim=1)[0].view(64,1)
## Double DQNs
#argmax on Target W
best_actions_by_local_nn = torch.max(
self.qnetwork_local(next_states).detach(), dim=1)[1].unsqueeze(1)
action_values_by_target_nn = self.qnetwork_target(
next_states).detach().gather(1, best_actions_by_local_nn)
Q_target = rewards + gamma * action_values_by_target_nn * (1 - dones)
Q_pred = self.qnetwork_local(states).gather(1, actions)
optimizer.zero_grad()
loss = loss_fn(Q_pred, Q_target)
loss.backward()
optimizer.step()
# print("Loss=", loss.item())
# print("Loss=", loss,
# "Local params L2=", torch.norm(self.qnetwork_local.parameters(), 2),
# "Local params grad L2=", torch.norm(self.qnetwork_local.parameters().grad, 2))
# with torch.no_grad():
# for param in self.qnetwork_local.parameters():
# param -= learning_rate * param.grad
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class DDQNAgentPrioExpReplay:
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=PARAM.LR)
# Replay memory
self.memory = PrioritizedReplayBuffer(action_size, 20000,
PARAM.BATCH_SIZE, 0,
PARAM.PROBABILITY_EXPONENT)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
self.eps = 1
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % PARAM.UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > PARAM.BATCH_SIZE:
experiences, experience_indices, importance_weights = self.memory.sample(
)
self.learn(experiences, experience_indices, importance_weights,
PARAM.GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
self.eps = eps
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def get_ddqn_targets(self, next_states, rewards, gamma, dones):
# get best action according to online value function approximation
q_online = self.qnetwork_local(next_states).detach()
q_online = q_online.argmax(1)
# get value of target function at position of best online action
q_target = self.qnetwork_target(next_states).detach()
q_target = q_target.index_select(1, q_online)[:, 0]
# reshape
q_target = q_target.unsqueeze(1)
# calculate more correct q-value given the current reward
Q_targets = rewards + (gamma * q_target * (1 - dones))
return Q_targets
def learn(self, experiences, experience_indices, importance_weights,
gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
Q_targets = self.get_ddqn_targets(next_states, rewards, gamma, dones)
# Get expected Q values
q_exp = self.qnetwork_local(states)
# print(q_exp)
# gets the q values along dimension 1 according to the actions, which is used as index
# >>> t = torch.tensor([[1,2],[3,4]])
# >>> torch.gather(t, 1, torch.tensor([[0],[1]]))
# tensor([[ 1],
# [ 4]])
q_exp = q_exp.gather(1, actions)
# print(q_exp)
error = torch.abs(q_exp - Q_targets)
with torch.no_grad():
# update priority
# we need ".cpu()" here because the values need to be copied to memory before converting them to numpy,
# else they are just present in the GPU
errors = np.squeeze(error.cpu().data.numpy())
self.memory.set_priorities(experience_indices, errors)
# compute loss
squared_error = torch.mul(error, error)
with torch.no_grad():
w = torch.from_numpy(
importance_weights**(1 - self.eps)).float().to(device)
w = w.detach()
squared_error = torch.squeeze(squared_error)
weighted_squared_error = torch.mul(squared_error, w)
loss = torch.mean(weighted_squared_error)
#loss = F.mse_loss(q_exp, Q_targets)
# reset optimizer gradient
self.optimizer.zero_grad()
# do backpropagation
loss.backward()
# do optimize step
self.optimizer.step()
# ------------------- update target network ------------------- #
# according to the algorithm in
# https://proceedings.neurips.cc/paper/2010/file/091d584fced301b442654dd8c23b3fc9-Paper.pdf
# one should update randomly in ether direction
#update_direction = np.random.binomial(1, 0.5)
#if update_direction == 0:
# self.soft_update(self.qnetwork_local, self.qnetwork_target, PARAM.TAU)
#else:
# self.soft_update(self.qnetwork_target, self.qnetwork_local, PARAM.TAU)
self.soft_update(self.qnetwork_local, self.qnetwork_target, PARAM.TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, buffer_size=int(1e5), batch_size=64,
gamma=0.99, tau=1e-3, lr=5e-4, update_every=4,
double_dqn=False, dueling_dqn=False, prioritized_replay=False):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
buffer_size (int): replay buffer size
batch_size (int): minibatch size
gamma (float): discount factor
tau (float): for soft update of target parameters
lr (float): learning rate
update_every (int): how often to update the network
double_dqn (bool) use double Q-network when 'True'
dueling_dqn (bool): use dueling Q-network when 'True'
prioritized_replay (bool): use prioritized replay buffer when 'True'
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.buffer_size = buffer_size
self.batch_size = batch_size
self.gamma = gamma
self.tau = tau
self.lr = lr
self.update_every = update_every
self.double_dqn = double_dqn
self.dueling_dqn = dueling_dqn
self.prioritized_replay = prioritized_replay
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed, dueling_dqn=dueling_dqn).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed, dueling_dqn=dueling_dqn).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=self.lr)
# Replay memory
self.memory = ReplayBuffer(action_size, self.buffer_size, self.batch_size, seed, prioritized_replay=prioritized_replay)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
def act(self, state, eps=0.):
"""Returns actions for a given state as per the current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using a given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
if self.prioritized_replay:
states, actions, rewards, next_states, dones, indices, weights = experiences
else:
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q-values (for next states) from target model
if self.double_dqn:
# Use local model to choose an action, and target model to evaluate that action
Q_local_max = self.qnetwork_local(next_states).detach().max(1)[1].unsqueeze(1)
Q_targets_next = self.qnetwork_target(next_states).gather(1, Q_local_max)
else:
Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# Compute Q-targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q-values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
if self.prioritized_replay:
priorities = np.sqrt(loss.detach().cpu().data.numpy())
self.memory.update_priorities(indices, priorities)
loss = loss * weights
loss = loss.mean()
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): where weights will be copied from
target_model (PyTorch model): where weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
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 Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, lr_decay=0.9999):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
if USE_DUELING_NETWORK:
self.qnetwork_local = DuelingQNetwork(state_size, action_size,
seed, [128, 32],
[64, 32]).to(device)
self.qnetwork_target = DuelingQNetwork(state_size, action_size,
seed, [128, 32],
[64, 32]).to(device)
self.qnetwork_target.eval()
else:
self.qnetwork_local = QNetwork(state_size,
action_size,
seed,
fc1_units=128,
fc2_units=32).to(device)
self.qnetwork_target = QNetwork(state_size,
action_size,
seed,
fc1_units=128,
fc2_units=32).to(device)
self.qnetwork_target.eval()
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.lr_scheduler = optim.lr_scheduler.ExponentialLR(
self.optimizer, lr_decay)
# Replay memory
if USE_PRIORITIZED_REPLAY_BUFFER:
self.memory = PrioritizedReplayBuffer(action_size,
BUFFER_SIZE,
BATCH_SIZE,
seed,
device,
alpha=0.6,
beta=0.4,
beta_scheduler=1.0)
else:
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones, w = experiences
if USE_DOUBLE_DQN:
self.qnetwork_local.eval()
Q_local = self.qnetwork_local(next_states)
greedy_actions = torch.argmax(Q_local, axis=1).unsqueeze(1)
self.qnetwork_local.train()
Q_targets_next = self.qnetwork_target(next_states)
Q_targets_next = Q_targets_next.gather(1, greedy_actions)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
else:
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets_next = Q_targets_next.max(dim=1, keepdim=True)[0]
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
if USE_PRIORITIZED_REPLAY_BUFFER:
Q_targets.sub_(Q_expected)
Q_targets.squeeze_()
Q_targets.pow_(2)
with torch.no_grad():
td_error = Q_targets.detach()
td_error.pow_(0.5)
self.memory.update_priorities(td_error)
Q_targets.mul_(w)
loss = Q_targets.mean()
else:
loss = F.mse_loss(Q_expected, Q_targets)
# Mback-propagation
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.lr_scheduler.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
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)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval() # this change the local net to eval mode
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train(
) # this just return the local net back to train mode
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
# Get max predicted Q values (for next states) from target model
target_q_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
"""
# disregard action, get best value!
# why so many next states? answer: the qnetwork will return each corresponding next states action, the max will pick from each the best action
# explanation on detach (https://discuss.pytorch.org/t/detach-no-grad-and-requires-grad/16915/7)
"""
# Compute Q targets for current states
target_q = rewards + (gamma * target_q_next * (1 - dones))
# Get expected Q values from local model
expected_q = self.qnetwork_local(states).gather(1, actions)
"""
this uses gather instead of detach like target since it only give a s*** to action taken
# explanation on gather (https://stackoverflow.com/questions/50999977/what-does-the-gather-function-do-in-pytorch-in-layman-terms)
"""
# Compute loss
loss = F.mse_loss(expected_q, target_q)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
net = nn.DataParallel(self.qnetwork_local)
if torch.cuda.is_available():
print("using GPUs!")
net.cuda()
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
# target net update
# Get max predicted Q values (for next states) from target model
# double means two w, Compute Q targets for current states
double_Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# choose the index of the max Q, get the action of policy
best_actions = np.argmax(double_Q_targets_next, dim=1, keepdim=True)
# implement best action to obtain Q(S_t+1, Q_argmax(S_t+1, a, w), w^-)
Q_target_next = self.qnetwork_local(states).gather(
1, best_actions).detach()
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
#logger.info('mse: {}'.format(delta))
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step() # evolutionary step - increase survival chances
#logger.info('avg reward: {} mse:{}'.format(delta, np.mean(experiences.rewards())))
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
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.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.critic = QNetwork(self.num_inputs, self.action_space,
args.hidden_size)
self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
# 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
if self.policy_type == "Gaussian":
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.evaluate(state)
else:
self.policy.eval()
_, _, _, action, _ = self.policy.evaluate(state)
#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)
mask_batch = torch.FloatTensor(np.float32(mask_batch))
reward_batch = reward_batch.unsqueeze(
1) # reward_batch = [batch_size, 1]
mask_batch = mask_batch.unsqueeze(1) # mask_batch = [batch_size, 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.evaluate(
state_batch)
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
if self.policy_type == "Gaussian":
"""
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
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.
"""
next_state_action, _, _, _, _, = self.policy.evaluate(
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
"""
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.detach())
q2_value_loss = F.mse_loss(expected_q2_value, next_q_value.detach())
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 BEARQL(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = args.tau
self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(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(device)
hard_update(self.critic_target, self.critic)
self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(device)
self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
self.policy_target = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(device)
hard_update(self.policy_target, self.policy)
# dual_lambda
self.dual_lambda = args.init_dual_lambda
self.dual_step_size = args.dual_step_size
self.cost_epsilon = args.cost_epsilon
# coefficient_weight assigned to ensemble variance term
self.coefficient_weight = args.coefficient_weight
self.dual_grad_times = args.dual_grad_times
# used in evaluation
def select_action(self, state):
# sample multiple policies and perform a greedy maximization of Q over these policies
with torch.no_grad():
state = torch.FloatTensor(state.reshape(1, -1)).repeat(10, 1).to(device)
# state = torch.FloatTensor(state.reshape(1, -1)).to(device)
action, _, mean = self.policy.sample(state)
# q1, q2 = self.critic(state, action)
q1, q2, q3 = self.critic(state, action)
ind = q1.max(0)[1]
return action[ind].cpu().data.numpy().flatten()
# return action.cpu().data.numpy().flatten()
# MMD functions
def compute_kernel(self, x, y, sigma):
batch_size = x.shape[0]
x_size = x.shape[1]
y_size = y.shape[1]
dim = x.shape[2]
tiled_x = x.view(batch_size, x_size, 1, dim).repeat([1, 1, y_size, 1])
tiled_y = y.view(batch_size, 1, y_size, dim).repeat([1, x_size, 1, 1])
return torch.exp(-(tiled_x - tiled_y).pow(2).sum(dim=3) / (2 * sigma))
def compute_mmd(self, x, y, sigma=20.):
x_kernel = self.compute_kernel(x, x, sigma)
y_kernel = self.compute_kernel(y, y, sigma)
xy_kernel = self.compute_kernel(x, y, sigma)
square_mmd = x_kernel.mean((1, 2)) + y_kernel.mean((1, 2)) - 2 * xy_kernel.mean((1, 2))
return square_mmd
def train(self, prior, memory, batch_size, m=4, n=4):
# Sample replay buffer / batch
state_np, action_np, reward_np, next_state_np, mask_np = memory.sample(batch_size=batch_size)
state_batch = torch.FloatTensor(state_np).to(device)
next_state_batch = torch.FloatTensor(next_state_np).to(device)
action_batch = torch.FloatTensor(action_np).to(device)
reward_batch = torch.FloatTensor(reward_np).to(device).unsqueeze(1)
mask_batch = torch.FloatTensor(mask_np).to(device).unsqueeze(1)
# Critic Training
with torch.no_grad():
# Duplicate state 10 times
next_state_rep = torch.FloatTensor(np.repeat(next_state_np, 10, axis=0)).to(device)
# Soft Clipped Double Q-learning
next_state_action, _, _ = self.policy_target.sample(next_state_rep)
target_Q1, target_Q2, target_Q3 = self.critic_target(next_state_rep, next_state_action)
target_cat = torch.cat([target_Q1, target_Q2, target_Q3], 1)
target_Q = 0.75 * target_cat.min(1)[0] + 0.25 * target_cat.max(1)[0]
target_Q = target_Q.view(batch_size, -1).max(1)[0].view(-1, 1)
next_q_value = reward_batch + mask_batch * self.gamma * target_Q
qf1, qf2, qf3 = self.critic(state_batch, action_batch) # ensemble of k Q-functions
q_loss = F.mse_loss(qf1, next_q_value) + F.mse_loss(qf2, next_q_value) + F.mse_loss(qf3, next_q_value)
self.critic_optim.zero_grad()
q_loss.backward()
self.critic_optim.step()
# Actor Training
with torch.no_grad():
state_rep_m = torch.FloatTensor(np.repeat(state_np, m, axis=0)).to(device)
state_rep_n = torch.FloatTensor(np.repeat(state_np, n, axis=0)).to(device)
for i in range(self.dual_grad_times):
prior_a_rep, _, _ = prior.sample(state_rep_n)
prior_a_rep = prior_a_rep.view(batch_size, n, -1)
pi_rep, _, _ = self.policy.sample(state_rep_m)
pi_rep = pi_rep.view(batch_size, m, -1)
mmd_dist = self.compute_mmd(prior_a_rep, pi_rep)
pi, _, _ = self.policy.sample(state_batch)
qf1_pi, qf2_pi, qf3_pi = self.critic(state_batch, pi)
qf_cat = torch.cat([qf1_pi, qf2_pi, qf3_pi], 1)
# min_qf_pi = torch.min(qf1_pi, qf2_pi) # used in TD3
# use conservative estimate of Q as used in BEAR
qf_mean = qf_cat.mean(1)
qf_var = qf_cat.var(1)
min_qf_pi = qf_mean - self.coefficient_weight * qf_var.sqrt() # used in BEAR
policy_loss = -(min_qf_pi - self.dual_lambda*mmd_dist).mean()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
# Dual Lambda Training
self.dual_gradients = mmd_dist.mean().item() - self.cost_epsilon
self.dual_lambda += self.dual_step_size * self.dual_gradients
self.dual_lambda = np.clip(self.dual_lambda, np.power(np.e, -5), np.power(np.e, 10))
# Update Target Networks
soft_update(self.critic_target, self.critic, self.tau)
soft_update(self.policy_target, self.policy, self.tau)
return q_loss.item(), policy_loss.item(), self.dual_lambda, mmd_dist.mean().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/BEAR_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/BEAR_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))
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, checkpoint_path=None):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
# Load the model only when the checkpoint is available
if checkpoint_path is not None:
self.qnetwork_local.load_state_dict(torch.load(checkpoint_path))
print("Checkpoint loaded successfully")
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
self.optimizer.zero_grad()
#target
with torch.no_grad():
#Double DQN
ddqn_max_indices = self.qnetwork_local(next_states).max(dim=1)[1]
target_op = self.qnetwork_target(next_states)
target_op = target_op.gather(1, ddqn_max_indices.view(-1, 1))
'''
# DQN
target_op = self.qnetwork_target(next_states).max(dim=1)[0].view(-1,1)
'''
targets = rewards + target_op * (1 - dones) * gamma
predictions = self.qnetwork_local(states)
predictions = predictions.gather(1, actions.view(-1, 1))
loss = torch.nn.MSELoss()(predictions, targets)
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
def __init__(self,
state_size,
action_size,
hidden_layers,
buffer_size=int(1e6),
batch_size=32,
gamma=.99,
tau=1,
lr=2.5e-4,
update_local=4,
update_target=10000,
ddqn=False,
seed=1):
"""Initialize Agent object
Params
======
state_size (int): Dimension of states
action_size (int): Dimension of actions
hidden_layers (list of ints): number of nodes in the hidden layers
buffer_size (int): size of replay buffer
batch_size (int): size of sample
gamma (float): discount factor
tau (float): (soft) update of target parameters
lr (float): learning rate
update_local (int): update local after every x steps
update_target (int): update target after every x steps
ddqn (boolean): Double Deep Q-Learning
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Hyperparameters
self.buffer_size = buffer_size # replay buffer
self.batch_size = batch_size # minibatch size
self.gamma = gamma # discount factor
self.tau = tau # (soft) update of target parameters
self.lr = lr # learning rate
self.update_local = update_local # update local network after every x steps
self.update_target = update_target # update target network with local network weights
# Q Network
self.qnet_local = \
QNetwork(state_size, action_size, hidden_layers, seed).to(device)
self.qnet_target = \
QNetwork(state_size, action_size, hidden_layers, seed).to(device)
self.optimizer = optim.Adam(self.qnet_local.parameters(), lr=lr)
# Replay buffer
self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed)
# Initialize time step
self.t_step = 0
# Double Deep Q-Learning flag
self.ddqn = ddqn
def step(self, state, action, reward, next_state, done):
# Save experience in replay buffer
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE LOCAL time steps
self.t_step += 1
if self.t_step % self.update_local == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
sample = self.memory.sample()
if self.t_step % self.update_target == 0:
do_target_update = True
else:
do_target_update = False
self.__learn(sample, self.gamma, do_target_update)
def act(self, state, epsilon=0):
"""Returns action given a state according to local Q Network (current policy)
Params
======
state (array_like): current state
epsilon (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnet_local.eval()
with torch.no_grad():
action_values = self.qnet_local(state)
self.qnet_local.train()
# Epsilon greedy action selection
if random.random() > epsilon:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def __learn(self, sample, gamma, do_target_update):
"""Update value parameters using given batch of sampled experiences tuples
Params
======
sample (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = sample
if not self.ddqn:
# Get max predicted Q values (for next states) from target model
Q_targets_next = \
self.qnet_target(next_states).detach().max(1)[0].unsqueeze(1)
else:
# Get actions (for next states) with max Q values from local net
next_actions = \
self.qnet_local(next_states).detach().max(1)[1].unsqueeze(1)
# Get predicted Q values from target model
Q_targets_next = \
self.qnet_target(next_states).gather(1, next_actions)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnet_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update target network
if do_target_update:
self.__target_net_update(self.qnet_local, self.qnet_target,
self.tau)
def __target_net_update(self, local_net, target_net, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param \
in zip(target_net.parameters(), local_net.parameters()):
target_param.data.\
copy_(tau*local_param.data + (1.0 - tau)*target_param.data)
def get_info(self):
output = """
Replay Buffer size: {} \n
Batch size: {} \n
Discout factor: {} \n
tau: {} \n
Learning Rate: {} \n
Update local network after every {} steps \n
Update target network with local network parameters after every {} steps \n
DDQN: {}
"""
print(
output.format(self.buffer_size, self.batch_size, self.gamma,
self.tau, self.lr, self.update_local,
self.update_target, self.ddqn))
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
for i in range(len(experiences)): # loop through all experiences in batch
self.optimizer.zero_grad() # zero out gradient
if dones[i]:
y = rewards[i] # if the experience was the end of the episode y = this reward
else:
# if the experience is not the end of the episode
# y = immediate reward + gamma*(reward of best action in next state)
y = rewards[i] + gamma*(max(self.qnetwork_target(next_states[i])))
q = self.qnetwork_local(states[i]) # determine value of curent state in local copy of model
z = (y - q[actions[i]])**2 # calculate loss due to this experience
z.backward() # compute gradient and update weights using optimizer
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)