def reset(self):
        self.noise.reset()

    def learn(self, experiences, gamma):
                actions_next = self.actor_target(next_states)
        Q_targets_next = self.critic_target(next_states, actions_next)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
        Q_expected = self.critic_local(states, actions)
        critic_loss = F.mse_loss(Q_expected, Q_targets)
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        actions_pred = self.actor_local(states)
        actor_loss = -self.critic_local(states, actions_pred).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        self.soft_update(self.critic_local, self.critic_target, TAU)
        self.soft_update(self.actor_local, self.actor_target, TAU)