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package rl.dqn.reinforcement.dqn.nn

import java.util

import org.deeplearning4j.rl4j.learning.Learning
import org.deeplearning4j.rl4j.learning.sync.Transition
import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning
import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning.{QLConfiguration, QLStepReturn}
import org.deeplearning4j.rl4j.mdp.MDP
import org.deeplearning4j.rl4j.network.dqn.{DQN, IDQN}

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unstacked_output_type operator()(
      const std::vector<Tensor>& step_inputs,
      const hidden_type& input_hidden,
      const cell_params& params,
      bool pre_compute_input = false) const {
    std::vector<Tensor> step_outputs;
    auto hidden = input_hidden;

    for (const auto& input : step_inputs) {
    	std::cout << "step input " << std::endl;

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  output_type operator()(
      const Tensor& inputs,
      const hidden_type& input_hidden,
      const cell_params& params) const override {
    if (inputs.device().is_cpu()) {
      const auto inputs_w = params.linear_ih(inputs);
      auto hidden = cell_(inputs_w, input_hidden, params, true);
      return {hidden_as_output(hidden), hidden};
    }
    auto unstacked_output = (*this)(inputs.unbind(0), input_hidden, params);

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/*
 * ex10.cu
 *
 *  Created on: Aug 23, 2022
 *      Author:
 */


#include <stdlib.h>
#include <string.h>

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void testLockFloatAdd() {
	int n = 1;
	int nBytes = n * sizeof(float);
	float* ha = (float*)malloc(nBytes);
	*ha = 0;

	float *da = nullptr;
    CHECK(cudaMalloc((float **)&da, nBytes));
    CHECK(cudaMemcpy(da, ha, nBytes, cudaMemcpyHostToDevice));