# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Python spiel example."""

import logging
from absl import app
from absl import flags

from open_spiel.python import rl_environment
from open_spiel.python.algorithms import random_agent

FLAGS = flags.FLAGS

flags.DEFINE_string("game", "kuhn_poker", "Name of the game.")
flags.DEFINE_integer("num_players", 2, "Number of players.")
flags.DEFINE_integer("num_episodes", 2, "Number of episodes.")


def print_iteration(time_step, player_id, action=None):
  """Print TimeStep information."""
  obs = time_step.observations
  logging.info("Player: %s", player_id)
  if time_step.first():
    logging.info("Info state: %s, - - %s", obs["info_state"][player_id],
                 time_step.step_type)
  else:
    logging.info("Info state: %s, %s %s %s", obs["info_state"][player_id],
                 time_step.rewards[player_id], time_step.discounts[player_id],
                 time_step.step_type)
  if action is not None:
    logging.info("Action taken: %s", action)
  logging.info("-" * 80)


def main_loop(unused_arg):
  """RL main loop example."""
  logging.info("Registered games: %s", rl_environment.registered_games())
  logging.info("Creating game %s", FLAGS.game)

  env_configs = {"players": FLAGS.num_players} if FLAGS.num_players else {}
  env = rl_environment.Environment(FLAGS.game, **env_configs)
  num_actions = env.action_spec()["num_actions"]

  agents = [
      random_agent.RandomAgent(player_id=i, num_actions=num_actions)
      for i in range(FLAGS.num_players)
  ]

  logging.info("Env specs: %s", env.observation_spec())
  logging.info("Action specs: %s", env.action_spec())

  for cur_episode in range(FLAGS.num_episodes):
    logging.info("Starting episode %s", cur_episode)
    time_step = env.reset()
    while not time_step.last():
      pid = time_step.observations["current_player"]

      if env.is_turn_based:
        agent_output = agents[pid].step(time_step)
        action_list = [agent_output.action]
      else:
        agents_output = [agent.step(time_step) for agent in agents]
        action_list = [agent_output.action for agent_output in agents_output]

      print_iteration(time_step, pid, action_list)
      time_step = env.step(action_list)

    # Episode is over, step all agents with final state.
    for agent in agents:
      agent.step(time_step)

    # Print final state of end game.
    for pid in range(env.num_players):
      print_iteration(time_step, pid)


if __name__ == "__main__":
  app.run(main_loop)
