def run_step(agents: List[Agent], gs: GameState):
assert (not gs.is_game_over())
active_player_index = gs.get_active_player()
old_scores = gs.get_scores().copy()
action = agents[active_player_index].act(gs)
gs.step(active_player_index, action)
new_scores = gs.get_scores()
rewards = new_scores - old_scores
for i, agent in enumerate(agents):
agent.observe(rewards[i], gs.is_game_over(), i)
def run_for_n_games_and_return_stats(
agents: List[Agent], gs: GameState,
games_count: int) -> (np.ndarray, np.ndarray):
total_scores = np.zeros_like(gs.get_scores())
for _ in range(games_count):
gs_copy = gs.clone()
run_to_the_end(agents, gs_copy)
total_scores += gs_copy.get_scores()
return total_scores, total_scores / games_count
def run_for_n_games_and_return_max(agents: List[Agent], gs: GameState,
games_count: int) -> np.ndarray:
old_and_new_scores = np.ones((2, len(gs.get_scores()))) * -9999.9
for _ in range(games_count):
gs_copy = gs.clone()
run_to_the_end(agents, gs_copy)
new_scores = gs_copy.get_scores()
old_and_new_scores[1, :] = new_scores
old_and_new_scores[0, :] = np.max(old_and_new_scores, axis=0)
return old_and_new_scores[0, :]
def run_for_n_games_and_return_stats(
agents: List[Agent],
gs: GameState,
games_count: int,
shuffle_players: bool = False) -> (np.ndarray, np.ndarray):
total_scores = np.zeros_like(gs.get_scores())
agents_order = np.arange(len(agents))
agents_copy = agents
if shuffle_players:
agents_copy = agents.copy()
for _ in range(games_count):
gs_copy = gs.clone()
if shuffle_players:
agents_copy = agents.copy()
shuffle(agents_order)
for i in agents_order:
agents_copy[i] = agents[agents_order[i]]
run_to_the_end(agents_copy, gs_copy)
total_scores += gs_copy.get_scores()[agents_order]
return total_scores, total_scores / games_count