simple_dqn.hpp

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#ifndef MLPACK_METHODS_RL_SIMPLE_DQN_HPP
#define MLPACK_METHODS_RL_SIMPLE_DQN_HPP
 
#include <mlpack/prereqs.hpp>
#include <mlpack/methods/ann/ffn.hpp>
#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>
#include <mlpack/methods/ann/layer/layer.hpp>
#include <mlpack/methods/ann/loss_functions/mean_squared_error.hpp>
 
namespace mlpack {
namespace rl {
 
using namespace mlpack::ann;
 
template<
  typename OutputLayerType = MeanSquaredError<>,
  typename InitType = GaussianInitialization,
  typename NetworkType = FFN<OutputLayerType, InitType>
>
class SimpleDQN
{
 public:
  SimpleDQN() : network(), isNoisy(false)
  { /* Nothing to do here. */ }
 
  SimpleDQN(const int inputDim,
            const int h1,
            const int h2,
            const int outputDim,
            const bool isNoisy = false,
            InitType init = InitType(),
            OutputLayerType outputLayer = OutputLayerType()):
      network(outputLayer, init),
      isNoisy(isNoisy)
  {
    network.Add(new Linear<>(inputDim, h1));
    network.Add(new ReLULayer<>());
    if (isNoisy)
    {
      noisyLayerIndex.push_back(network.Model().size());
      network.Add(new NoisyLinear<>(h1, h2));
      network.Add(new ReLULayer<>());
      noisyLayerIndex.push_back(network.Model().size());
      network.Add(new NoisyLinear<>(h2, outputDim));
    }
    else
    {
      network.Add(new Linear<>(h1, h2));
      network.Add(new ReLULayer<>());
      network.Add(new Linear<>(h2, outputDim));
    }
  }
 
  SimpleDQN(NetworkType& network, const bool isNoisy = false):
      network(network),
      isNoisy(isNoisy)
  { /* Nothing to do here. */ }
 
  void Predict(const arma::mat state, arma::mat& actionValue)
  {
    network.Predict(state, actionValue);
  }
 
  void Forward(const arma::mat state, arma::mat& target)
  {
    network.Forward(state, target);
  }
 
  void ResetParameters()
  {
    network.ResetParameters();
  }
 
  void ResetNoise()
  {
    for (size_t i = 0; i < noisyLayerIndex.size(); i++)
    {
      boost::get<NoisyLinear<>*>
          (network.Model()[noisyLayerIndex[i]])->ResetNoise();
    }
  }
 
  const arma::mat& Parameters() const { return network.Parameters(); }
  arma::mat& Parameters() { return network.Parameters(); }
 
  void Backward(const arma::mat state, arma::mat& target, arma::mat& gradient)
  {
    network.Backward(state, target, gradient);
  }
 
 private:
  NetworkType network;
 
  bool isNoisy;
 
  std::vector<size_t> noisyLayerIndex;
};
 
} // namespace rl
} // namespace mlpack
 
#endif