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)