def __init__(self, game_state:Quoridor, policy_output, value_output):
# _counts is the number of times we've taken some action *from this state*. Initialized to all zeros. Stored
# as a torch tensor over all possible actions, to be later masked with the set of legal actions
self._counts = torch.zeros(3, 9, 9)
self._total_reward = torch.zeros(3, 9, 9)
self._policy = policy_output
self._value = value_output
self._legal_mask = encode_actions_to_planes(game_state.all_legal_moves(), game_state.current_player)
self._player = game_state.current_player
self._key = game_state.hash_key()
self._children = {}
self.__flagged = False
col) + "v"
if temperature < 1e-6:
# Do max operation instead of unstable low-temperature manipulations
idx = torch.argmax(policy_planes)
else:
idx = torch.multinomial(policy_planes.flatten()**temperature,
num_samples=1)
return _idx_to_action(idx.item())
if __name__ == '__main__':
# mini test
q = Quoridor()
legal_moves = q.all_legal_moves(partial_check=False)
print("INITIAL STATE LEGAL MOVES ({} of them):".format(len(legal_moves)))
print(legal_moves)
for mv in legal_moves:
planes = encode_actions_to_planes(mv, q.current_player)
print("=========== {} ============".format(mv))
print(planes)
mv2 = sample_action(planes, 0)
print(mv2)
assert mv2 == mv, "Failed to encode/decode {}".format(mv)
# Test that just sampling random moves leads to some illegal moves getting selected (this is expected)
random_actions, masked_random_actions = [''] * 100, [''] * 100
legal_mask = encode_actions_to_planes(legal_moves, q.current_player)
for i in range(100):
