class Agent():
def __init__(self, state_size, action_size, 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)
self.qnetwork_local.load_model("./dqn_LL_model data.pickle")
self.qnetwork_target.load_model("./dqn_LL_model data.pickle")
# 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
self.loss = 0
self.loss_list = []
def step(self, state, action, reward, next_state, done, t_step):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = t_step
if self.t_step % UPDATE_EVERY == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > 100 * 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.
"""
action_values = self.qnetwork_local.forward(state)
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values)
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
"""
states, actions, rewards, next_states, dones = experiences
for time in range(BATCH_SIZE):
# compute Q_target from the target network inputing next_state
Q_target_av = np.max(
self.qnetwork_target.forward(next_states[time]))
Q_target = rewards[time] + gamma * (Q_target_av) * (
1 - dones[time]) # if done, than the second will not be added
# compute the Q_expected
Q_expected = self.qnetwork_local.forward(
states[time]
) # get q value for corrosponding action along dimension 1 of 64,4 matrix
self.qnetwork_local.backward(Q_target, "MSE", actions[time])
self.loss_list.append((Q_target - Q_expected[actions[time]])**2)
self.loss = np.mean(self.loss_list)
self.qnetwork_local.step()
self.loss_list.clear()
# update target network #
if self.t_step % UPDATE_FREQUENCY == 0:
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 = tau*θ_local + (1 - tau)*θ_target
"""
self.qnetwork_target.soft_update(local_model, TAU)
