class Agent(nn.Module):
def __init__(self,
input_shape,
num_actions,
device,
PATH,
gamma=0.95,
learning_rate=0.001,
replay_size=10000,
batch_size=128):
super(Agent, self).__init__()
self.device = device
self.PATH = PATH
self.gamma = gamma
self.lr = learning_rate
self.num_actions = num_actions
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 200
self.epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * math.exp(-1. * frame_idx /
epsilon_decay)
self.replay_size = replay_size
self.batch_size = batch_size
self.policy_net = DQN(input_shape, num_actions).to(device)
self.target_net = DQN(input_shape, num_actions).to(device)
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=self.lr)
self.replay_buffer = ReplayBuffer(replay_size)
self.best_loss = 9999
def declare_networks(self):
self.policy_net = DQN(input_shape, num_actions).to(device)
self.target_net = DQN(input_shape, num_actions).to(device)
def declare_memory(self):
self.replay_buffer = ReplayBuffer(self.replay_size)
def compute_loss(self):
if len(self.replay_buffer) > self.batch_size:
state, action, reward, next_state, done = self.replay_buffer.sample(
self.batch_size)
state = Variable(torch.Tensor(np.array(state))).to(self.device)
action = Variable(torch.LongTensor(action)).to(self.device)
reward = Variable(torch.Tensor(np.array(reward))).to(self.device)
next_state = Variable(torch.Tensor(np.array(next_state))).to(
self.device)
done = Variable(torch.Tensor(np.array(done))).to(self.device)
q_values = self.policy_net(state)
q_value = q_values.gather(1, action.unsqueeze(1)).squeeze(1)
with torch.no_grad():
next_q_values = self.policy_net(next_state)
next_q_state_values = self.target_net(next_state)
next_q_value = next_q_state_values.gather(
1,
torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
expected_q_value = reward + self.gamma * next_q_value * (1 - done)
# MSE
loss = (q_value - expected_q_value.detach()).pow(2).mean()
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
if loss < self.best_loss:
self.model_save()
self.best_loss = loss
return loss.item()
else:
return 9999
def append_buffer(self, state, action, reward, next_state, done):
self.replay_buffer.push(state, action, reward, next_state, done)
def get_action(self, state, episode):
epsilon = self.epsilon_by_frame(episode)
with torch.no_grad():
if random.random() > epsilon:
#state = Variable(torch.Tensor(np.array(state))).to(device)
q_value = self.policy_net(state)
action = q_value.max(1)[1].item()
else:
action = np.random.randint(0, self.num_actions)
return action
def update_target_model(self):
self.target_net.load_state_dict(self.policy_net.state_dict())
def model_save(self):
torch.save(
{
'model_state_dict': self.policy_net.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
}, self.PATH)
def model_load(self):
if self.device == "cuda:0":
checkpoint = torch.load(self.PATH)
else:
checkpoint = torch.load(self.PATH,
map_location=torch.device('cpu'))
self.policy_net.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
class Agent:
"""Reinforcement Learning Agent that learns using DDPG"""
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor Policy Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model params with local model params
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
# Noise Process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay Memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm Parameters
self.gamma = 0.99 # Discount Factor
self.tau = 0.01 # for Soft Update of Target Parameters
self.score = 0
self.best_score = -np.inf
self.count = 0
self.total_reward = 0.0
def reset_episode(self):
self.count = 0
self.total_reward = 0.0
self.noise.reset()
state = self.task.reset()
self.last_state = state
return state
def step(self, action, reward, next_state, done):
"""Save Experience / Reward"""
self.count += 1
self.total_reward += reward
self.memory.add(self.last_state, action, reward, next_state, done)
# Learn if enough samples are present in memory
if len(self.memory) > self.batch_size:
self.score = reward
experiences = self.memory.sample()
self.learn(experiences)
# Roll over the last state and action
self.last_state = next_state
def act(self, state):
"""Returns actions for given state(s) as per current policy."""
state = np.reshape(state, [-1, self.state_size])
action = self.actor_local.model.predict(state)[0]
return list(action +
self.noise.sample()) # Add some noise for exploration
def learn(self, experiences):
"""Update policy and value parameters using given batch of experience tuples."""
self.score = self.total_reward / float(
self.count) if self.count else 0.0
# Convert experience tuples to separate arrays for each element (states, actions, rewards, etc.)
states = np.vstack([e.state for e in experiences if e is not None])
actions = np.array([e.action for e in experiences
if e is not None]).astype(np.float32).reshape(
-1, self.action_size)
rewards = np.array([e.reward for e in experiences if e is not None
]).astype(np.float32).reshape(-1, 1)
dones = np.array([e.done for e in experiences
if e is not None]).astype(np.uint8).reshape(-1, 1)
next_states = np.vstack(
[e.next_state for e in experiences if e is not None])
# Get predicted next-state actions and Q values from target models
# Q_targets_next = critic_target(next_state, actor_target(next_state))
action_next = self.actor_target.model.predict_on_batch(next_states)
Q_targets_next = self.critic_target.model.predict_on_batch(
[next_states, action_next])
# Compute Q targets for current states and train critic model (local)
Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones)
self.critic_local.model.train_on_batch(x=[states, actions],
y=Q_targets)
# Train the Actor Model
action_gradients = np.reshape(
self.critic_local.get_action_gradients([states, actions, 0]),
(-1, self.action_size))
self.actor_local.train_fn([states, action_gradients, 1])
self.soft_update(self.critic_local.model, self.critic_target.model)
self.soft_update(self.actor_local.model, self.actor_target.model)
if self.score > self.best_score:
self.best_score = self.score
def soft_update(self, local_model, target_model):
"""Soft Update Model Parameters"""
local_weights = np.array(local_model.get_weights())
target_weights = np.array(target_model.get_weights())
assert len(local_weights) == len(target_weights)
new_weights = self.tau * local_weights + (1 -
self.tau) * target_weights
target_model.set_weights(new_weights)