sac.hpp

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#ifndef MLPACK_METHODS_RL_SAC_HPP
#define MLPACK_METHODS_RL_SAC_HPP

#include <mlpack/prereqs.hpp>

#include "replay/random_replay.hpp"
#include <mlpack/methods/ann/activation_functions/tanh_function.hpp>
#include <mlpack/methods/ann/loss_functions/mean_squared_error.hpp>
#include <mlpack/methods/ann/visitor/parameters_visitor.hpp>
#include "training_config.hpp"

namespace mlpack {
namespace rl {

template <
  typename EnvironmentType,
  typename QNetworkType,
  typename PolicyNetworkType,
  typename UpdaterType,
  typename ReplayType = RandomReplay<EnvironmentType>
>
class SAC
{
 public:
  using StateType = typename EnvironmentType::State;

  using ActionType = typename EnvironmentType::Action;

  SAC(TrainingConfig& config,
      QNetworkType& learningQ1Network,
      PolicyNetworkType& policyNetwork,
      ReplayType& replayMethod,
      UpdaterType qNetworkUpdater = UpdaterType(),
      UpdaterType policyNetworkUpdater = UpdaterType(),
      EnvironmentType environment = EnvironmentType());

  ~SAC();

  void SoftUpdate(double rho);

  void Update();

  void SelectAction();

  double Episode();

  size_t& TotalSteps() { return totalSteps; }
  const size_t& TotalSteps() const { return totalSteps; }

  StateType& State() { return state; }
  const StateType& State() const { return state; }

  const ActionType& Action() const { return action; }

  bool& Deterministic() { return deterministic; }
  const bool& Deterministic() const { return deterministic; }


 private:
  TrainingConfig& config;

  QNetworkType& learningQ1Network;
  QNetworkType learningQ2Network;

  QNetworkType targetQ1Network;
  QNetworkType targetQ2Network;

  PolicyNetworkType& policyNetwork;

  ReplayType& replayMethod;

  UpdaterType qNetworkUpdater;
  #if ENS_VERSION_MAJOR >= 2
  typename UpdaterType::template Policy<arma::mat, arma::mat>*
      qNetworkUpdatePolicy;
  #endif

  UpdaterType policyNetworkUpdater;
  #if ENS_VERSION_MAJOR >= 2
  typename UpdaterType::template Policy<arma::mat, arma::mat>*
      policyNetworkUpdatePolicy;
  #endif

  EnvironmentType environment;

  size_t totalSteps;

  StateType state;

  ActionType action;

  bool deterministic;

  mlpack::ann::MeanSquaredError<> lossFunction;
};

} // namespace rl
} // namespace mlpack

// Include implementation
#include "sac_impl.hpp"
#endif