def test_python_and_cpp_bot(self):
game = pyspiel.load_game("kuhn_poker")
bots = [
pyspiel.make_uniform_random_bot(0, 1234),
uniform_random.UniformRandomBot(1, np.random.RandomState(4321)),
]
results = np.array([
pyspiel.evaluate_bots(game.new_initial_state(), bots, iteration)
for iteration in range(10000)
])
average_results = np.mean(results, axis=0)
np.testing.assert_allclose(average_results, [0.125, -0.125], atol=0.1)
def bot_evaluation(game, bots, num_evaluations):
"""Returns a tuple (wins, losses, draws) for player 2."""
wins, losses, draws = 0, 0, 0
for i in range(num_evaluations):
[_, result] = pyspiel.evaluate_bots(game.new_initial_state(), bots, i)
if result == 0:
draws += 1
elif result == 1:
wins += 1
else:
losses += 1
return (wins, losses, draws)
def test_passing_params(self):
game = pyspiel.load_game("tic_tac_toe")
bots = [
pyspiel.load_bot("fixed_action_preference",
game,
player=0,
params={"actions": "0:1:2"}),
pyspiel.load_bot("fixed_action_preference",
game,
player=1,
params={"actions": "3:4"}),
]
result = pyspiel.evaluate_bots(game.new_initial_state(), bots, seed=0)
self.assertEqual(result, [1, -1]) # Player 0 wins.
def test_cpp_mcts_bot(self):
game = pyspiel.load_game("tic_tac_toe")
bots = [
pyspiel.MCTSBot(game, pyspiel.RandomRolloutEvaluator(1, 0), 2.0,
100, 100, False, 42, False)
] * 2
_ = np.array([
pyspiel.evaluate_bots(game.new_initial_state(), bots, iteration)
for iteration in range(10)
])
# Do a search directly, and inspect the values.
state = game.new_initial_state()
search_node = bots[0].mcts_search(state)
for child in search_node.children:
print(
f"Child action {child.action}, total reward: {child.total_reward}"
+ f", explore count: {child.explore_count}")
# Similar way to achieve the above.
print(f"Children string: {search_node.children_str(state)}")
print(f"Best child: {search_node.best_child().to_string(state)}")
