class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size: int, action_size: int, seed: int,
n_agent: int):
"""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.n_agent = n_agent
self.seed = random.seed(seed)
self.global_step = 0
self.update_step = 0
# Initialize actor and critic local and target networks
self.actor = Actor(state_size,
action_size,
seed,
ACTOR_NETWORK_LINEAR_SIZES,
batch_normalization=ACTOR_BATCH_NORM).to(device)
self.actor_target = Actor(
state_size,
action_size,
seed,
ACTOR_NETWORK_LINEAR_SIZES,
batch_normalization=ACTOR_BATCH_NORM).to(device)
self.critic = Critic(state_size,
action_size,
seed,
CRITIC_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_second = Critic(
state_size,
action_size,
seed,
CRITIC_SECOND_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_second_target = Critic(
state_size,
action_size,
seed,
CRITIC_SECOND_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_target = Critic(
state_size,
action_size,
seed,
CRITIC_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.actor_optimizer = optim.Adam(self.actor.parameters(),
lr=ACTOR_LEARNING_RATE)
self.critic_optimizer = optim.Adam(self.critic.parameters(),
lr=CRITIC_LEARNING_RATE)
self.critic_second_optimizer = optim.Adam(
self.critic_second.parameters(), lr=CRITIC_LEARNING_RATE)
# 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] * n_agent
self.noise = OUNoise(action_size, seed, decay_period=50)
# Copy parameters from local network to target network
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(param.data)
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data)
for target_param, param in zip(self.critic_second_target.parameters(),
self.critic_second.parameters()):
target_param.data.copy_(param.data)
def step(self, state: np.array, action, reward, next_state, done, i_agent):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step[i_agent] = (self.t_step[i_agent] + 1) % UPDATE_EVERY
# Learn, if enough samples are available in memory every UPDATE_EVERY
if len(self.memory) > BATCH_SIZE and (not any(self.t_step)):
for _ in range(LEARN_TIMES):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def noise_reset(self):
self.noise.reset()
def save_model(self, checkpoint_path: str = "./checkpoints/"):
torch.save(self.actor.state_dict(), f"{checkpoint_path}/actor.pt")
torch.save(self.critic.state_dict(), f"{checkpoint_path}/critic.pt")
def load_model(self, checkpoint_path: str = "./checkpoints/checkpoint.pt"):
self.actor.load_state_dict(torch.load(f"{checkpoint_path}/actor.pt"))
self.critic.load_state_dict(torch.load(f"{checkpoint_path}/critic.pt"))
def act(self, states: np.array, step: int):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
"""
self.global_step += 1
if self.global_step < WARM_UP_STEPS:
action_values = np.random.rand(self.n_agent, self.action_size)
return action_values
states = torch.from_numpy(states).float().to(device)
self.actor.eval()
with torch.no_grad():
action_values = self.actor(states).cpu().data.numpy()
self.actor.train()
action_values = [
self.noise.get_action(action, t=step) for action in action_values
]
return action_values
def learn(self, experiences: tuple, gamma=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
"""
self.update_step += 1
states, actions, rewards, next_states, dones = experiences
# Critic loss
mask = torch.tensor(1 - dones).detach().to(device)
Q_values = self.critic(states, actions)
Q_values_second = self.critic_second(states, actions)
next_actions = self.actor_target(next_states)
next_Q = torch.min(
self.critic_target(next_states, next_actions.detach()),
self.critic_second_target(next_states, next_actions.detach()))
Q_prime = rewards + gamma * next_Q * mask
if self.update_step % CRITIC_UPDATE_EVERY == 0:
critic_loss = F.mse_loss(Q_values, Q_prime.detach())
critic_second_loss = F.mse_loss(Q_values_second, Q_prime.detach())
# Update first critic network
self.critic_optimizer.zero_grad()
critic_loss.backward()
if CRITIC_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.critic.parameters(),
CRITIC_GRADIENT_CLIPPING_VALUE)
self.critic_optimizer.step()
# Update second critic network
self.critic_second_optimizer.zero_grad()
critic_second_loss.backward()
if CRITIC_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.critic_second.parameters(),
CRITIC_GRADIENT_CLIPPING_VALUE)
self.critic_second_optimizer.step()
# Actor loss
if self.update_step % POLICY_UPDATE_EVERY == 0:
policy_loss = -self.critic(states, self.actor(states)).mean()
# Update actor network
self.actor_optimizer.zero_grad()
policy_loss.backward()
if ACTOR_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.actor.parameters(),
ACTOR_GRADIENT_CLIPPING_VALUE)
self.actor_optimizer.step()
self.actor_soft_update()
self.critic_soft_update()
self.critic_second_soft_update()
def actor_soft_update(self, tau: float = TAU):
"""Soft update for actor target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
def critic_soft_update(self, tau: float = TAU):
"""Soft update for critic target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.detach_()
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
def critic_second_soft_update(self, tau: float = TAU):
"""Soft update for critic target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.critic_second_target.parameters(),
self.critic_second.parameters()):
target_param.detach_()
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
class DDPGAgent(object):
"""
General class for DDPG agents (policy, critic, target policy, target
critic, exploration noise)
"""
def __init__(self, num_in_pol, num_out_pol, num_in_critic, hidden_dim_actor=120,
hidden_dim_critic=64,lr_actor=0.01,lr_critic=0.01,batch_size=64,
max_episode_len=100,tau=0.02,gamma = 0.99,agent_name='one', discrete_action=False):
"""
Inputs:
num_in_pol (int): number of dimensions for policy input
num_out_pol (int): number of dimensions for policy output
num_in_critic (int): number of dimensions for critic input
"""
self.policy = Actor(num_in_pol, num_out_pol,
hidden_dim=hidden_dim_actor,
discrete_action=discrete_action)
self.critic = Critic(num_in_pol, 1,num_out_pol,
hidden_dim=hidden_dim_critic)
self.target_policy = Actor(num_in_pol, num_out_pol,
hidden_dim=hidden_dim_actor,
discrete_action=discrete_action)
self.target_critic = Critic(num_in_pol, 1,num_out_pol,
hidden_dim=hidden_dim_critic)
hard_update(self.target_policy, self.policy)
hard_update(self.target_critic, self.critic)
self.policy_optimizer = Adam(self.policy.parameters(), lr=lr_actor)
self.critic_optimizer = Adam(self.critic.parameters(), lr=lr_critic,weight_decay=0)
self.policy = self.policy.float()
self.critic = self.critic.float()
self.target_policy = self.target_policy.float()
self.target_critic = self.target_critic.float()
self.agent_name = agent_name
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
#self.replay_buffer = ReplayBuffer(1e7)
self.replay_buffer = ReplayBufferOption(500000,self.batch_size,12)
self.max_replay_buffer_len = batch_size * max_episode_len
self.replay_sample_index = None
self.niter = 0
self.eps = 5.0
self.eps_decay = 1/(250*5)
self.exploration = OUNoise(num_out_pol)
self.discrete_action = discrete_action
self.num_history = 2
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
def reset_noise(self):
if not self.discrete_action:
self.exploration.reset()
def scale_noise(self, scale):
if self.discrete_action:
self.exploration = scale
else:
self.exploration.scale = scale
def act(self, obs, explore=False):
"""
Take a step forward in environment for a minibatch of observations
Inputs:
obs : Observations for this agent
explore (boolean): Whether or not to add exploration noise
Outputs:
action (PyTorch Variable): Actions for this agent
"""
#obs = obs.reshape(1,48)
state = Variable(torch.Tensor(obs),requires_grad=False)
self.policy.eval()
with torch.no_grad():
action = self.policy(state)
self.policy.train()
# continuous action
if explore:
action += Variable(Tensor(self.eps * self.exploration.sample()),requires_grad=False)
action = torch.clamp(action, min=-1, max=1)
return action
def step(self, agent_id, state, action, reward, next_state, done,t_step):
self.states.append(state)
self.actions.append(action)
self.rewards.append(reward)
self.next_states.append(next_state)
self.dones.append(done)
#self.replay_buffer.add(state, action, reward, next_state, done)
if t_step % self.num_history == 0:
# Save experience / reward
self.replay_buffer.add(self.states, self.actions, self.rewards, self.next_states, self.dones)
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
# Learn, if enough samples are available in memory
if len(self.replay_buffer) > self.batch_size:
obs, acs, rews, next_obs, don = self.replay_buffer.sample()
self.update(agent_id ,obs, acs, rews, next_obs, don,t_step)
def update(self, agent_id, obs, acs, rews, next_obs, dones ,t_step, logger=None):
obs = torch.from_numpy(obs).float()
acs = torch.from_numpy(acs).float()
rews = torch.from_numpy(rews[:,agent_id]).float()
next_obs = torch.from_numpy(next_obs).float()
dones = torch.from_numpy(dones[:,agent_id]).float()
acs = acs.view(-1,2)
# --------- update critic ------------ #
self.critic_optimizer.zero_grad()
all_trgt_acs = self.target_policy(next_obs)
target_value = (rews + self.gamma *
self.target_critic(next_obs,all_trgt_acs) *
(1 - dones))
actual_value = self.critic(obs,acs)
vf_loss = MSELoss(actual_value, target_value.detach())
# Minimize the loss
vf_loss.backward()
#torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1)
self.critic_optimizer.step()
# --------- update actor --------------- #
self.policy_optimizer.zero_grad()
if self.discrete_action:
curr_pol_out = self.policy(obs)
curr_pol_vf_in = gumbel_softmax(curr_pol_out, hard=True)
else:
curr_pol_out = self.policy(obs)
curr_pol_vf_in = curr_pol_out
pol_loss = -self.critic(obs,curr_pol_vf_in).mean()
#pol_loss += (curr_pol_out**2).mean() * 1e-3
pol_loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1)
self.policy_optimizer.step()
self.update_all_targets()
self.eps -= self.eps_decay
self.eps = max(self.eps, 0)
if logger is not None:
logger.add_scalars('agent%i/losses' % self.agent_name,
{'vf_loss': vf_loss,
'pol_loss': pol_loss},
self.niter)
def update_all_targets(self):
"""
Update all target networks (called after normal updates have been
performed for each agent)
"""
soft_update(self.critic, self.target_critic, self.tau)
soft_update(self.policy, self.target_policy, self.tau)
def get_params(self):
return {'policy': self.policy.state_dict(),
'critic': self.critic.state_dict(),
'target_policy': self.target_policy.state_dict(),
'target_critic': self.target_critic.state_dict(),
'policy_optimizer': self.policy_optimizer.state_dict(),
'critic_optimizer': self.critic_optimizer.state_dict()}
def load_params(self, params):
self.policy.load_state_dict(params['policy'])
self.critic.load_state_dict(params['critic'])
self.target_policy.load_state_dict(params['target_policy'])
self.target_critic.load_state_dict(params['target_critic'])
self.policy_optimizer.load_state_dict(params['policy_optimizer'])
self.critic_optimizer.load_state_dict(params['critic_optimizer'])
class MADDPG(object):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def __init__(self, state_dim, action_dim, max_action, agent_n, logger):
# 存在于 GPU 的神经网络
self.actor = Actor(state_dim, action_dim,
max_action).to(self.device) # origin_network
self.actor_target = Actor(state_dim, action_dim,
max_action).to(self.device) # target_network
self.actor_target.load_state_dict(self.actor.state_dict(
)) # initiate actor_target with actor's parameters
# pytorch 中的 tensor 默认requires_grad 属性为false，即不参与梯度传播运算，特别地，opimizer中模型参数是会参与梯度优化的
self.actor_optimizer = optim.Adam(
self.actor.parameters(),
pdata.LEARNING_RATE) # 以pdata.LEARNING_RATE指定学习率优化actor中的参数
self.critic = CriticCentral(agent_n).to(self.device)
self.critic_target = CriticCentral(agent_n).to(self.device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = optim.Adam(self.critic.parameters(),
pdata.LEARNING_RATE)
# self.replay_buffer 取消：每个Agent不再有独立的经验池
self.writer = SummaryWriter(pdata.DIRECTORY + 'runs')
self.num_critic_update_iteration = 0
self.num_actor_update_iteration = 0
self.num_training = 0
self.logger = logger
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
# numpy.ndarray.flatten(): 返回一个 ndarray对象的copy，并且将该ndarray压缩成一维数组
action = self.actor(state).cpu().data.numpy().flatten()
return action # 未归一化的
def select_target_actions(self, states):
# state 的输入必须是归一化的[[],[]] np.ndarray
united_state = torch.FloatTensor(states).to(self.device)
action = self.actor_target(united_state).cpu().data.numpy()
return action # 未归一化的
def select_current_actions(self, states):
united_state = torch.FloatTensor(states).to(self.device)
action = self.actor(united_state).cpu().data.numpy()
return action # 未归一化的
# update the parameters in actor network and critic network
def update(self, central_replay_buffer_ma, agent_list, agent_idx):
critic_loss_list = []
actor_performance_list = []
# Sample replay buffer: [(united_normalized_states, united_normalized_next_states, united__normalized_action, [reward_1, ...], done), (...)]
x, y, u, r, d = central_replay_buffer_ma.sample(
pdata.BATCH_SIZE
) # 随机获取 batch_size 个五元组样本(sample random minibatch)
# TODO: 从这里以下要改造成MADDPG的范式
next_actions = [
agent_list[i].select_target_actions(
y[:, i * pdata.STATE_DIMENSION:i * pdata.STATE_DIMENSION +
pdata.STATE_DIMENSION]) for i in range(len(agent_list))
]
next_actions = np.concatenate(next_actions, axis=1)
united_next_action_batch = torch.FloatTensor(next_actions).to(
self.device)
united_state_batch = torch.FloatTensor(x).to(self.device)
united_action_batch = torch.FloatTensor(u).to(self.device)
united_next_state_batch = torch.FloatTensor(y).to(self.device)
done = torch.FloatTensor(d).to(self.device)
reward_batch = torch.FloatTensor(r[:, agent_idx]) # torch.Size([64])
reward_batch = reward_batch[:,
None].to(self.device) # torch.Size([64,1])
target_Q = self.critic_target(
united_next_state_batch,
united_next_action_batch) # torch.Size([64,1])
target_Q = reward_batch + (
(1 - done) * pdata.GAMMA * target_Q).detach() # batch_size个维度
current_Q = self.critic(united_state_batch, united_action_batch)
critic_loss = F.mse_loss(current_Q, target_Q)
self.writer.add_scalar('critic_loss',
critic_loss,
global_step=self.num_critic_update_iteration)
# self.logger.write_to_log('critic_loss:{loss}'.format(loss=critic_loss))
# self.logger.add_to_critic_buffer(critic_loss.item())
critic_loss_list.append(critic_loss.item())
self.critic_optimizer.zero_grad() # zeros the gradient buffer
critic_loss.backward() # back propagation on a dynamic graph
self.critic_optimizer.step()
current_actions = [
agent_list[i].select_current_actions(
x[:, i * pdata.STATE_DIMENSION:i * pdata.STATE_DIMENSION +
pdata.STATE_DIMENSION]) for i in range(len(agent_list))
]
current_actions_batch = torch.FloatTensor(
np.concatenate(current_actions, axis=1)).to(self.device)
actor_loss = -self.critic(united_state_batch,
current_actions_batch).mean()
self.writer.add_scalar('actor_performance',
actor_loss,
global_step=self.num_actor_update_iteration)
# self.logger.write_to_log('actor_loss:{loss}'.format(loss=actor_loss))
# self.logger.add_to_actor_buffer(actor_loss.item())
actor_performance_list.append(actor_loss.item())
# Optimize the actor
self.actor_optimizer.zero_grad(
) # Clears the gradients of all optimized torch.Tensor
actor_loss.backward()
self.actor_optimizer.step() # perform a single optimization step
# 这里是两个 target网络的 soft update
for param, target_param in zip(self.critic.parameters(),
self.critic_target.parameters()):
target_param.data.copy_(pdata.TAU * param.data +
(1 - pdata.TAU) * target_param.data)
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_(pdata.TAU * param.data +
(1 - pdata.TAU) * target_param.data)
self.num_actor_update_iteration += 1
self.num_critic_update_iteration += 1
actor_performance = np.mean(np.array(actor_performance_list)).item()
self.logger.add_to_actor_buffer(actor_performance)
critic_loss = np.mean(critic_loss_list).item()
self.logger.add_to_critic_buffer(critic_loss)
def save(self, mark_str):
torch.save(self.actor.state_dict(),
pdata.DIRECTORY + mark_str + '_actor_ma.pth')
torch.save(self.critic.state_dict(),
pdata.DIRECTORY + mark_str + '_critic_ma.pth')
def load(self, mark_str):
file_actor = pdata.DIRECTORY + mark_str + '_actor_ma.pth'
file_critic = pdata.DIRECTORY + mark_str + '_critic_ma.pth'
if os.path.exists(file_actor) and os.path.exists(file_critic):
self.actor.load_state_dict(torch.load(file_actor))
self.critic.load_state_dict(torch.load(file_critic))
class DDPG(object):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
veer = 'straight'
origin = 'west'
# state_dim : 状态维度
# action_dim： 动作维度
# max_action：动作限制向量
def __init__(self, state_dim, action_dim, max_action, origin_str, veer_str,
logger):
# 存在于 GPU 的神经网络
self.actor = Actor(state_dim, action_dim,
max_action).to(self.device) # origin_network
self.actor_target = Actor(state_dim, action_dim,
max_action).to(self.device) # target_network
self.actor_target.load_state_dict(self.actor.state_dict(
)) # initiate actor_target with actor's parameters
# pytorch 中的 tensor 默认requires_grad 属性为false，即不参与梯度传播运算，特别地，opimizer中模型参数是会参与梯度优化的
self.actor_optimizer = optim.Adam(
self.actor.parameters(),
pdata.LEARNING_RATE) # 以pdata.LEARNING_RATE指定学习率优化actor中的参数
self.critic = Critic(state_dim, action_dim).to(self.device)
self.critic_target = Critic(state_dim, action_dim).to(self.device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = optim.Adam(self.critic.parameters(),
pdata.LEARNING_RATE)
# self.replay_buffer = Replay_buffer() # initiate replay-buffer
self.replay_buffer = FilterReplayBuffer()
self.writer = SummaryWriter(pdata.DIRECTORY + 'runs')
self.num_critic_update_iteration = 0
self.num_actor_update_iteration = 0
self.num_training = 0
self.veer = veer_str
self.origin = origin_str
self.logger = logger
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
# numpy.ndarray.flatten(): 返回一个 ndarray对象的copy，并且将该ndarray压缩成一维数组
action = self.actor(state).cpu().data.numpy().flatten()
return action
# update the parameters in actor network and critic network
# 只有 replay_buffer 中的 storage 超过了样本数量才会调用 update函数
def update(self):
critic_loss_list = []
actor_performance_list = []
for it in range(pdata.UPDATE_ITERATION):
# Sample replay buffer
x, y, u, r, d = self.replay_buffer.sample(
pdata.BATCH_SIZE
) # 随机获取 batch_size 个五元组样本(sample random minibatch)
state = torch.FloatTensor(x).to(self.device)
action = torch.FloatTensor(u).to(self.device)
next_state = torch.FloatTensor(y).to(self.device)
done = torch.FloatTensor(d).to(self.device)
reward = torch.FloatTensor(r).to(self.device)
# Compute the target Q value —— Q(S', A') is an value evaluated with next_state and predicted action
# 这里的 target_Q 是 sample 个 一维tensor
target_Q = self.critic_target(next_state,
self.actor_target(next_state))
# detach(): Return a new tensor, detached from the current graph
# evaluate Q: targetQ = R + γQ'(s', a')
target_Q = reward + (
(1 - done) * pdata.GAMMA * target_Q).detach() # batch_size个维度
# Get current Q estimate
current_Q = self.critic(state, action) # 1 维
# Compute critic loss : a mean-square error
# 由论文，critic_loss 其实计算的是每个样本估计值与每个critic网络输出的均值方差
# torch.nn.functional.mse_loss 为计算tensor中各个元素的的均值方差
critic_loss = F.mse_loss(current_Q, target_Q)
self.writer.add_scalar(
'critic_loss',
critic_loss,
global_step=self.num_critic_update_iteration)
# self.logger.write_to_log('critic_loss:{loss}'.format(loss=critic_loss))
# self.logger.add_to_critic_buffer(critic_loss.item())
critic_loss_list.append(critic_loss.item())
# Optimize the critic
self.critic_optimizer.zero_grad() # zeros the gradient buffer
critic_loss.backward() # back propagation on a dynamic graph
self.critic_optimizer.step()
# Compute actor loss
# actor_loss：见论文中对公式 (6) 的理解
# mean()：对tensor对象求所有element的均值
# backward() 以梯度下降的方式更新参数，则将 actor_loss 设置为反向梯度，这样参数便往梯度上升方向更新
actor_loss = -self.critic(state, self.actor(state)).mean()
self.writer.add_scalar('actor_performance',
actor_loss,
global_step=self.num_actor_update_iteration)
# self.logger.write_to_log('actor_loss:{loss}'.format(loss=actor_loss))
# self.logger.add_to_actor_buffer(actor_loss.item())
actor_performance_list.append(actor_loss.item())
# Optimize the actor
self.actor_optimizer.zero_grad(
) # Clears the gradients of all optimized torch.Tensor
actor_loss.backward()
self.actor_optimizer.step() # perform a single optimization step
# 这里是两个 target网络的 soft update
for param, target_param in zip(self.critic.parameters(),
self.critic_target.parameters()):
target_param.data.copy_(pdata.TAU * param.data +
(1 - pdata.TAU) * target_param.data)
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_(pdata.TAU * param.data +
(1 - pdata.TAU) * target_param.data)
self.num_actor_update_iteration += 1
self.num_critic_update_iteration += 1
actor_performance = np.mean(np.array(actor_performance_list)).item()
self.logger.add_to_actor_buffer(actor_performance)
critic_loss = np.mean(critic_loss_list).item()
self.logger.add_to_critic_buffer(critic_loss)
def save(self, mark_str):
torch.save(self.actor.state_dict(),
pdata.DIRECTORY + mark_str + '_actor.pth')
torch.save(self.critic.state_dict(),
pdata.DIRECTORY + mark_str + '_critic.pth')
def load(self, mark_str):
file_actor = pdata.DIRECTORY + mark_str + '_actor.pth'
file_critic = pdata.DIRECTORY + mark_str + '_critic.pth'
if os.path.exists(file_actor) and os.path.exists(file_critic):
self.actor.load_state_dict(torch.load(file_actor))
self.critic.load_state_dict(torch.load(file_critic))
class DDPGAgent:
def __init__(self, state_size=24, action_size=2, seed=1, num_agents=2):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.num_agents = num_agents
# DDPG specific configuration
hidden_size = 512
self.CHECKPOINT_FOLDER = './'
# Defining networks
self.actor = Actor(state_size, hidden_size, action_size).to(device)
self.actor_target = Actor(state_size, hidden_size, action_size).to(device)
self.critic = Critic(state_size, self.action_size, hidden_size, 1).to(device)
self.critic_target = Critic(state_size, self.action_size, hidden_size, 1).to(device)
self.optimizer_actor = optim.Adam(self.actor.parameters(), lr=ACTOR_LR)
self.optimizer_critic = optim.Adam(self.critic.parameters(), lr=CRITIC_LR)
# Noise
self.noises = OUNoise((num_agents, action_size), seed)
# Initialize replay buffer
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
def act(self, state, add_noise=True):
'''
Returns action to be taken based on state provided as the input
'''
state = torch.from_numpy(state).float().to(device)
actions = np.zeros((self.num_agents, self.action_size))
self.actor.eval()
with torch.no_grad():
for agent_num, state in enumerate(state):
action = self.actor(state).cpu().data.numpy()
actions[agent_num, :] = action
self.actor.train()
if add_noise:
actions += self.noises.sample()
return np.clip(actions, -1, 1)
def reset(self):
self.noises.reset()
def learn(self, experiences):
'''
Trains the actor critic network using experiences
'''
states, actions, rewards, next_states, dones = experiences
# Update Critic
actions_next = self.actor_target(next_states)
# print(next_states.shape, actions_next.shape)
Q_targets_next = self.critic_target(next_states, actions_next)
Q_targets = rewards + GAMMA*Q_targets_next*(1-dones)
Q_expected = self.critic(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer_critic.zero_grad()
critic_loss.backward()
self.optimizer_critic.step()
# Update Actor
actions_pred = self.actor(states)
actor_loss = -self.critic(states, actions_pred).mean()
self.optimizer_actor.zero_grad()
actor_loss.backward()
self.optimizer_actor.step()
# Updating the local networks
self.soft_update(self.critic, self.critic_target)
self.soft_update(self.actor, self.actor_target)
def soft_update(self, model, model_target):
tau = TAU
for target_param, local_param in zip(model_target.parameters(), model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
def step(self, state, action, reward, next_state, done):
'''
Adds experience to memory and learns if the memory contains sufficient samples
'''
for i in range(self.num_agents):
self.memory.add(state[i, :], action[i, :], reward[i], next_state[i, :], done[i])
if len(self.memory) > BATCH_SIZE:
# print("Now Learning")
experiences = self.memory.sample()
self.learn(experiences)
def checkpoint(self):
'''
Saves the actor critic network on disk
'''
torch.save(self.actor.state_dict(), self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth')
torch.save(self.critic.state_dict(), self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth')
def load(self):
'''
Loads the actor critic network from disk
'''
self.actor.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth'))
self.actor_target.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth'))
self.critic.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth'))
self.critic_target.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth'))
class DDPG:
def __init__(self,
gamma,
tau,
hidden_size,
num_inputs,
action_space,
train_mode,
alpha,
replay_size,
normalize_obs=True,
normalize_returns=False,
critic_l2_reg=1e-2):
if torch.cuda.is_available():
self.device = torch.device('cuda')
torch.backends.cudnn.enabled = False
self.Tensor = torch.cuda.FloatTensor
else:
self.device = torch.device('cpu')
self.Tensor = torch.FloatTensor
self.alpha = alpha
self.train_mode = train_mode
self.num_inputs = num_inputs
self.action_space = action_space
self.critic_l2_reg = critic_l2_reg
self.actor = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
if self.train_mode:
self.actor_target = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.actor_perturbed = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.actor_optim = Adam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.critic_target = Critic(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.critic_optim = Adam(self.critic.parameters(),
lr=1e-3,
weight_decay=critic_l2_reg)
self.adversary_target = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary_perturbed = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary_optim = Adam(self.adversary.parameters(), lr=1e-4)
hard_update(
self.adversary_target,
self.adversary) # Make sure target is with the same weight
hard_update(self.actor_target,
self.actor) # Make sure target is with the same weight
hard_update(self.critic_target, self.critic)
self.gamma = gamma
self.tau = tau
self.normalize_observations = normalize_obs
self.normalize_returns = normalize_returns
if self.normalize_observations:
self.obs_rms = RunningMeanStd(shape=num_inputs)
else:
self.obs_rms = None
if self.normalize_returns:
self.ret_rms = RunningMeanStd(shape=1)
self.ret = 0
self.cliprew = 10.0
else:
self.ret_rms = None
self.memory = ReplayMemory(replay_size)
def eval(self):
self.actor.eval()
self.adversary.eval()
if self.train_mode:
self.critic.eval()
def train(self):
self.actor.train()
self.adversary.train()
if self.train_mode:
self.critic.train()
def select_action(self,
state,
action_noise=None,
param_noise=None,
mdp_type='mdp'):
state = normalize(
Variable(state).to(self.device), self.obs_rms, self.device)
if mdp_type != 'mdp':
if mdp_type == 'nr_mdp':
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1) * (1 - self.alpha)
if param_noise is not None:
adv_mu = self.adversary_perturbed(state)
else:
adv_mu = self.adversary(state)
adv_mu = adv_mu.data.clamp(-1, 1) * self.alpha
mu += adv_mu
else: # mdp_type == 'pr_mdp':
if np.random.rand() < (1 - self.alpha):
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1)
else:
if param_noise is not None:
mu = self.adversary_perturbed(state)
else:
mu = self.adversary(state)
mu = mu.data.clamp(-1, 1)
else:
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1)
return mu
def update_robust(self, state_batch, action_batch, reward_batch,
mask_batch, next_state_batch, adversary_update, mdp_type,
robust_update_type):
# TRAIN CRITIC
if robust_update_type == 'full':
if mdp_type == 'nr_mdp':
next_action_batch = (1 - self.alpha) * self.actor_target(next_state_batch) \
+ self.alpha * self.adversary_target(next_state_batch)
next_state_action_values = self.critic_target(
next_state_batch, next_action_batch)
else: # mdp_type == 'pr_mdp':
next_action_actor_batch = self.actor_target(next_state_batch)
next_action_adversary_batch = self.adversary_target(
next_state_batch)
next_state_action_values = self.critic_target(next_state_batch, next_action_actor_batch) * (
1 - self.alpha) \
+ self.critic_target(next_state_batch,
next_action_adversary_batch) * self.alpha
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch,
expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
value_loss = value_loss.item()
else:
value_loss = 0
if adversary_update:
# TRAIN ADVERSARY
self.adversary_optim.zero_grad()
if mdp_type == 'nr_mdp':
with torch.no_grad():
real_action = self.actor_target(next_state_batch)
action = (
1 - self.alpha
) * real_action + self.alpha * self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action)
else: # mdp_type == 'pr_mdp'
action = self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action) * self.alpha
adversary_loss = adversary_loss.mean()
adversary_loss.backward()
self.adversary_optim.step()
adversary_loss = adversary_loss.item()
policy_loss = 0
else:
if robust_update_type == 'full':
# TRAIN ACTOR
self.actor_optim.zero_grad()
if mdp_type == 'nr_mdp':
with torch.no_grad():
adversary_action = self.adversary_target(
next_state_batch)
action = (1 - self.alpha) * self.actor(
next_state_batch) + self.alpha * adversary_action
policy_loss = -self.critic(state_batch, action)
else: # mdp_type == 'pr_mdp':
action = self.actor(next_state_batch)
policy_loss = -self.critic(state_batch, action) * (
1 - self.alpha)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
else:
policy_loss = 0
adversary_loss = 0
return value_loss, policy_loss, adversary_loss
def update_non_robust(self, state_batch, action_batch, reward_batch,
mask_batch, next_state_batch):
# TRAIN CRITIC
next_action_batch = self.actor_target(next_state_batch)
next_state_action_values = self.critic_target(next_state_batch,
next_action_batch)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch,
expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
# TRAIN ACTOR
self.actor_optim.zero_grad()
action = self.actor(next_state_batch)
policy_loss = -self.critic(state_batch, action)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
return value_loss.item(), policy_loss, adversary_loss
def store_transition(self, state, action, mask, next_state, reward):
B = state.shape[0]
for b in range(B):
self.memory.push(state[b], action[b], mask[b], next_state[b],
reward[b])
if self.normalize_observations:
self.obs_rms.update(state[b].cpu().numpy())
if self.normalize_returns:
self.ret = self.ret * self.gamma + reward[b]
self.ret_rms.update(np.array([self.ret]))
if mask[b] == 0: # if terminal is True
self.ret = 0
def update_parameters(self,
batch_size,
mdp_type='mdp',
adversary_update=False,
exploration_method='mdp'):
transitions = self.memory.sample(batch_size)
batch = Transition(*zip(*transitions))
if mdp_type != 'mdp':
robust_update_type = 'full'
elif exploration_method != 'mdp':
robust_update_type = 'adversary'
else:
robust_update_type = None
state_batch = normalize(
Variable(torch.stack(batch.state)).to(self.device), self.obs_rms,
self.device)
action_batch = Variable(torch.stack(batch.action)).to(self.device)
reward_batch = normalize(
Variable(torch.stack(batch.reward)).to(self.device).unsqueeze(1),
self.ret_rms, self.device)
mask_batch = Variable(torch.stack(batch.mask)).to(
self.device).unsqueeze(1)
next_state_batch = normalize(
Variable(torch.stack(batch.next_state)).to(self.device),
self.obs_rms, self.device)
if self.normalize_returns:
reward_batch = torch.clamp(reward_batch, -self.cliprew,
self.cliprew)
value_loss = 0
policy_loss = 0
adversary_loss = 0
if robust_update_type is not None:
_value_loss, _policy_loss, _adversary_loss = self.update_robust(
state_batch, action_batch, reward_batch, mask_batch,
next_state_batch, adversary_update, mdp_type,
robust_update_type)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
if robust_update_type != 'full':
_value_loss, _policy_loss, _adversary_loss = self.update_non_robust(
state_batch, action_batch, reward_batch, mask_batch,
next_state_batch)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
self.soft_update()
return value_loss, policy_loss, adversary_loss
def soft_update(self):
soft_update(self.actor_target, self.actor, self.tau)
soft_update(self.adversary_target, self.adversary, self.tau)
soft_update(self.critic_target, self.critic, self.tau)
def perturb_actor_parameters(self, param_noise):
"""Apply parameter noise to actor model, for exploration"""
hard_update(self.actor_perturbed, self.actor)
params = self.actor_perturbed.state_dict()
for name in params:
if 'ln' in name:
pass
param = params[name]
param += torch.randn(param.shape).to(
self.device) * param_noise.current_stddev
"""Apply parameter noise to adversary model, for exploration"""
hard_update(self.adversary_perturbed, self.adversary)
params = self.adversary_perturbed.state_dict()
for name in params:
if 'ln' in name:
pass
param = params[name]
param += torch.randn(param.shape).to(
self.device) * param_noise.current_stddev
