如何使用 Go 语言进行深度强化学习研究？

type DQN struct {
    // 神经网络的参数
    weights [][][][]float64 

    // 模型的超参数
    batch_size         int 
    gamma              float64 
    epsilon            float64 
    epsilon_min        float64 
    epsilon_decay      float64 
    learning_rate      float64 
    learning_rate_min  float64 
    learning_rate_decay float64 
}

func (dqn *DQN) Train(env Environment, episodes int) {
    for e := 0; e < episodes; e++ {
        state := env.Reset()
        for {
            // 选择一个行动
            action := dqn.SelectAction(state)

            // 执行该行动
            next_state, reward, done := env.Step(action)

            // 将元组（记忆）存入经验回放缓冲区
            dqn.ReplayBuffer.Add(state, action, reward, next_state, done)

            // 从经验回放缓冲区中采样一批元组
            experiences := dqn.ReplayBuffer.Sample(dqn.BatchSize)

            // 用这批元组来训练神经网络
            dqn.Update(experiences)

            // 更新状态
            state = next_state

            // 判断是否终止
            if done {
                break
            }
        }

        // 调整超参数
        dqn.AdjustHyperparameters()
    }
}

func (dqn *DQN) Update(experiences []Experience) {
    // 计算目标 Q 值
    targets := make([][]float64, dqn.BatchSize)
    for i, e := range experiences {
        target := make([]float64, len(dqn.weights[len(dqn.weights)-1][0]))
        copy(target, dqn.Predict(e.State))
        if e.Done {
            target[e.Action] = e.Reward
        } else {
            max_q := dqn.Predict(e.NextState)
            target[e.Action] = e.Reward + dqn.Gamma*max_q
        }
        targets[i] = target
    }

    // 计算 Q 值的梯度
    grads := dqn.Backpropagate(experiences, targets)

    // 根据梯度更新神经网络的参数
    for i, grad := range grads {
        for j, g := range grad {
            for k, gg := range g {
                dqn.weights[i][j][k] -= dqn.LearningRate * gg
            }
        }
    }
}

func (dqn *DQN) Predict(state []float64) []float64 {
    input := state
    for i, w := range dqn.weights {
        output := make([]float64, len(w[0]))
        for j, ww := range w {
            dot := 0.0
            for k, val := range ww {
                dot += val * input[k]
            }
            output[j] = relu(dot)
        }
        input = output
        if i != len(dqn.weights)-1 {
            input = append(input, bias)
        }
    }
    return input
}