/* *****************************************************************************
 * A.L.E (Arcade Learning Environment)
 * Copyright (c) 2009-2013 by Yavar Naddaf, Joel Veness, Marc G. Bellemare,
 *  Matthew Hausknecht, and the Reinforcement Learning and Artificial Intelligence
 *  Laboratory
 * Released under the GNU General Public License; see License.txt for details.
 *
 * Based on: Stella  --  "An Atari 2600 VCS Emulator"
 * Copyright (c) 1995-2007 by Bradford W. Mott and the Stella team
 *
 * *****************************************************************************
 *  sharedLibraryInterfaceExample.cpp
 *
 *  Sample code for running an agent with the shared library interface.
 **************************************************************************** */

#include <iostream>
#include <ale_interface.hpp>

#ifdef __USE_SDL
#include <SDL.h>
#endif

using namespace std;

int main(int argc, char** argv) {
  if (argc < 2) {
    std::cerr << "Usage: " << argv[0] << " rom_file" << std::endl;
    return 1;
  }

  ale::ALEInterface ale;

  // Get & Set the desired settings
  ale.setInt("random_seed", 123);
  //The default is already 0.25, this is just an example
  ale.setFloat("repeat_action_probability", 0.25);

#ifdef __USE_SDL
  ale.setBool("display_screen", true);
  ale.setBool("sound", true);
#endif

  // Load the ROM file. (Also resets the system for new settings to
  // take effect.)
  ale.loadROM(argv[1]);

  // Get the vector of legal actions
  ale::ActionVect legal_actions = ale.getLegalActionSet();

  // Play 10 episodes
  for (int episode = 0; episode < 10; episode++) {
    float totalReward = 0;
    while (!ale.game_over()) {
      ale::Action a = legal_actions[rand() % legal_actions.size()];
      // Apply the action and get the resulting reward
      float reward = ale.act(a);
      totalReward += reward;
    }
    cout << "Episode " << episode << " ended with score: " << totalReward
         << endl;
    ale.reset_game();
  }

  return 0;
}