def update()

in maddpg/trainer/maddpg.py [0:0]

• 28 lines of code
• 6 McCabe index (conditional complexity)

    def update(self, agents, t):
        if len(self.replay_buffer) < self.max_replay_buffer_len: # replay buffer is not large enough
            return
        if not t % 100 == 0:  # only update every 100 steps
            return

        self.replay_sample_index = self.replay_buffer.make_index(self.args.batch_size)
        # collect replay sample from all agents
        obs_n = []
        obs_next_n = []
        act_n = []
        index = self.replay_sample_index
        for i in range(self.n):
            obs, act, rew, obs_next, done = agents[i].replay_buffer.sample_index(index)
            obs_n.append(obs)
            obs_next_n.append(obs_next)
            act_n.append(act)
        obs, act, rew, obs_next, done = self.replay_buffer.sample_index(index)

        # train q network
        num_sample = 1
        target_q = 0.0
        for i in range(num_sample):
            target_act_next_n = [agents[i].p_debug['target_act'](obs_next_n[i]) for i in range(self.n)]
            target_q_next = self.q_debug['target_q_values'](*(obs_next_n + target_act_next_n))
            target_q += rew + self.args.gamma * (1.0 - done) * target_q_next
        target_q /= num_sample
        q_loss = self.q_train(*(obs_n + act_n + [target_q]))

        # train p network
        p_loss = self.p_train(*(obs_n + act_n))

        self.p_update()
        self.q_update()

        return [q_loss, p_loss, np.mean(target_q), np.mean(rew), np.mean(target_q_next), np.std(target_q)]