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 = DQNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = DQNetwork(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 YellowBananaThief:
""" A smart agent that interacts with the environment to pick up yellow bananas"""
def __init__(self,
state_size,
action_size,
seed=0,
buffer_size=100000,
batch_size=64,
update_frequency=2,
gamma=.99,
learning_rate=5e-4,
tau=1e-3):
self.state_size = state_size
self.action_size = action_size
self.random = random.seed(seed)
self.batch_size = batch_size
self.memory = ReplayBuffer(self.action_size, buffer_size, batch_size,
seed)
self.time_step = 0
self.update_frequency = update_frequency
self.qnetwork_local = DQNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = DQNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=learning_rate)
# hyper-parameters
self.gamma = gamma
self.tau = tau
def act(self, state, epsilon):
""" Returns an epsilon greedy action to take in the current state
:param state: The current state in the environment
:param epsilon: Epsilon value to apply epsilon-greedy action selection
"""
def action_probabilities(action_vals, eps, num_actions):
""" Determine the epsilon probabilities of choosing actions """
probs = np.ones(num_actions, dtype=float) * (eps / num_actions)
best_action = np.argmax(action_vals)
probs[best_action] += (1. - eps)
return probs
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval(
) # get the network in evaluation mode and pull values from it
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train() # get the network back into train mode
action_probs = action_probabilities(action_values.cpu().data.numpy(),
epsilon, self.action_size)
return np.random.choice(np.arange(self.action_size), p=action_probs)
def step(self, state, action, reward, next_state, done):
""" Step forward to train the model """
self.memory.add(state, action, reward, next_state, done)
self.time_step = (self.time_step + 1) % self.update_frequency
if self.time_step == 0:
if len(self.memory) > self.batch_size:
# enough samples have been collected for learning from experience
experiences = self.memory.sample()
self.learn(experiences)
def learn(self, experiences):
""" Train the agent from a sample of experiences """
def soft_update(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)
states, actions, rewards, next_states, dones = experiences
# max predicted Q values for the next state
q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Q targets for current state
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 model loss
loss = F.mse_loss(q_expected, q_targets)
# minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)
def local_qnet(self):
""" Returns the trained model """
return self.qnetwork_local