class DDPGAgent(Agent):
"""
The DDPGAgent class implements a trainable DDPG agent.
Parameters
----------
logger: Logger
The variable specifies a logger for model management, plotting and printing.
obs_dim: int
The variable specifies the dimension of observation space vector.
action_space: ndarray
The variable specifies the action space of environment.
userconfig:
The variable specifies the config settings.
"""
def __init__(self, logger, obs_dim, action_space, userconfig):
super().__init__(logger=logger,
obs_dim=obs_dim,
action_dim=action_space.shape[0],
userconfig=userconfig)
self._observation_dim = obs_dim
self._action_space = action_space
self._action_n = action_space.shape[0]
self._config = {
"eps": 0.05,
"discount": 0.95,
"buffer_size": int(1e5),
"batch_size": 128,
"learning_rate_actor": 0.0002,
"learning_rate_critic": 0.0002,
"hidden_sizes": [256, 256],
'tau': 0.0001
}
self._config.update(userconfig)
self._eps = self._config['eps']
self._tau = self._config['tau']
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.eval_mode = False
if self._config['lr_milestones'] is None:
raise ValueError(
'lr_milestones argument cannot be None!\nExample: --lr_milestones=100 200 300'
)
lr_milestones = [
int(x) for x in (self._config['lr_milestones'][0]).split(' ')
]
# Critic
self.critic = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.critic_target = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
# Actor
self.actor = Actor(self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.actor_target = Actor(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
def eval(self):
self.eval_mode = True
def train_mode(self):
self.eval_mode = False
def act(self, observation, eps=0, evaluation=False):
state = torch.from_numpy(observation).float().to(self.device)
if eps is None:
eps = self._eps
if np.random.random() > eps or evaluation:
action = self.actor.forward(state)
action = action.detach().cpu().numpy()[0]
else:
action = self._action_space.sample()[:4]
return action
def schedulers_step(self):
self.critic.lr_scheduler.step()
self.critic_target.lr_scheduler.step()
self.actor.lr_scheduler.step()
self.actor_target.lr_scheduler.step()
def store_transition(self, transition):
self.buffer.add_transition(transition)
@staticmethod
def load_model(fpath):
with open(Path(fpath), 'rb') as inp:
return pickle.load(inp)
def train(self, total_step_counter, iter_fit=32):
losses = []
for i in range(iter_fit):
data = self.buffer.sample(batch_size=self._config['batch_size'])
s = torch.FloatTensor(np.stack(data[:, 0])).to(self.device)
s_next = torch.FloatTensor(np.stack(data[:, 3])).to(self.device)
a = torch.FloatTensor(np.stack(
data[:, 1])[:, None]).squeeze(dim=1).to(self.device)
rew = torch.FloatTensor(np.stack(
data[:, 2])[:, None]).squeeze(dim=1).to(self.device)
done = torch.FloatTensor(np.stack(
data[:, 4])[:,
None]).squeeze(dim=1).to(self.device) # done flag
Q_target = self.critic(s, a).squeeze(dim=1).to(self.device)
a_next = self.actor_target.forward(s_next)
Q_next = self.critic_target.forward(
s_next, a_next).squeeze(dim=1).to(self.device)
# target
targets = rew + self._config['discount'] * Q_next * (1.0 - done)
# optimize critic
targets = targets.to(self.device)
critic_loss = self.critic.loss(Q_target.float(), targets.float())
losses.append(critic_loss)
self.critic.optimizer.zero_grad()
critic_loss.backward()
self.critic.optimizer.step()
actions = self.actor.forward(s)
actor_loss = -self.critic.forward(s, actions).mean()
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# update
if (total_step_counter) % self._config['update_target_every'] == 0:
# optimize actor
soft_update(self.critic_target, self.critic, self._tau)
soft_update(self.actor_target, self.actor, self._tau)
return losses
class DDPGAgent:
def __init__(self,
env,
gamma,
tau,
buffer_maxlen,
critic_learning_rate,
actor_learning_rate,
max_action=1):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.env = env
self.obs_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
self.noise = OUNoise(env.action_space)
self.iter = 0.0
self.noisy = False
self.max_action = max_action
print(self.action_dim)
print(self.obs_dim)
# RL hyperparameters
self.env = env
self.gamma = gamma
self.tau = tau
# Initialize critic and actorr networks
self.critic = Critic(self.obs_dim, self.action_dim).to(self.device)
self.critic_target = Critic(self.obs_dim,
self.action_dim).to(self.device)
self.actor = Actor(self.obs_dim, self.action_dim,
self.max_action).to(self.device)
self.actor_target = Actor(self.obs_dim,
self.action_dim).to(self.device)
# Copy target network paramters for critic
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data)
# Set Optimization algorithms
self.critic_optimizer = optim.Adam(self.critic.parameters(),
lr=critic_learning_rate)
self.actor_optimizer = optim.Adam(self.actor.parameters(),
lr=actor_learning_rate)
self.replay_buffer = ExperienceReplayLog(buffer_maxlen)
def get_action(self, obs):
#print('obs;',obs)
if self.noisy == True:
state = torch.FloatTensor(obs).unsqueeze(0).to(self.device)
action = self.actor.forward(state)
action = action.squeeze(0).cpu().detach().numpy()
action = self.noise.get_action(action, self.iter)
self.iter = self.iter + 1
else:
state = torch.FloatTensor(obs).unsqueeze(0).to(self.device)
action = self.actor.forward(state)
action = action.squeeze(0).cpu().detach().numpy()
return action
def update(self, batch_size):
#Batch updates
states, actions, rewards, next_states, _ = self.replay_buffer.sample(
batch_size)
state_batch, action_batch, reward_batch, next_state_batch, masks = self.replay_buffer.sample(
batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
masks = torch.FloatTensor(masks).to(self.device)
# Q info updates
curr_Q = self.critic.forward(state_batch, action_batch)
next_actions = self.actor_target.forward(next_state_batch)
next_Q = self.critic_target.forward(next_state_batch,
next_actions.detach())
expected_Q = reward_batch + self.gamma * next_Q
# Update Critic network
q_loss = F.mse_loss(curr_Q, expected_Q.detach())
self.critic_optimizer.zero_grad()
q_loss.backward()
self.critic_optimizer.step()
# Update Actor network
policy_loss = -self.critic.forward(
state_batch, self.actor.forward(state_batch)).mean()
self.actor_optimizer.zero_grad()
policy_loss.backward()
self.actor_optimizer.step()
# Update Actor and Critic target networks
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(param.data * self.tau + target_param.data *
(1.0 - self.tau))
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data * self.tau + target_param.data *
(1.0 - self.tau))