def build_watten_train_4_512_agent(self,
agent_profile,
native_multi_gpu_enabled=False):
game = self.game_mapping[agent_profile.game]
x, y = game.get_observation_size()
nnet = WattenNNet4x512(x, y, 1, game.get_action_size())
agent_nnet = AgentNNet(nnet)
if agent_profile == EnvironmentSelector.WATTEN_AGENT_4_512_TRAIN:
print("Configuring build_watten_train_4_512_agent...")
return AgentMCTS(agent_nnet,
exp_rate=AgentMCTS.EXPLORATION_RATE_MEDIUM,
numMCTSSims=100,
max_predict_time=10,
num_threads=16)
elif agent_profile == EnvironmentSelector.WATTEN_AGENT_4_512_EVALUATE:
print("Configuring build_watten_evaluate_4_512_agent...")
return AgentMCTS(agent_nnet,
exp_rate=AgentMCTS.EXPLORATION_RATE_MEDIUM,
numMCTSSims=30,
max_predict_time=10,
num_threads=16,
name="build_watten_evaluate_4_512_agent")
elif agent_profile == EnvironmentSelector.WATTEN_AGENT_HUMAN:
return WattenHumanAgent(game)
elif agent_profile == EnvironmentSelector.WATTEN_AGENT_NNET:
agent_nnet.load(
os.path.abspath(
"../games/watten/training/best-4-512-new-4.h5"))
return agent_nnet
return None
def test_nn_agent_prediction(self):
sub_watten_game = WattenSubGame()
clone_sub_watten_game = sub_watten_game.clone()
x, y = sub_watten_game.get_observation_size()
nnet = SubWattenNNet(x, y, 1, sub_watten_game.get_action_size())
agent_nnet = AgentNNet(nnet)
agent_nnet.load("../../sub_watten/training/best.h5")
pi_values, v = agent_nnet.predict(sub_watten_game,
sub_watten_game.get_cur_player())
clone_pi_values, clone_v = agent_nnet.predict(
clone_sub_watten_game, clone_sub_watten_game.get_cur_player())
self.assertEqual(pi_values.all(), clone_pi_values.all())
self.assertEqual(v, clone_v)
def sub_watten_non_human_agent_for_total_watten(self):
game = WattenSubGame()
x, y = game.get_observation_size()
nnet = SubWattenNNet(x, y, 1, game.get_action_size())
agent_nnet = AgentNNet(nnet)
print('Building sub_watten non human agent for total_watten')
# load here best sub_watten model
try:
agent_nnet.load("games/sub_watten/training/default_nn/best.h5")
except OSError:
print("File not found with games/sub_watten/training/best.h5")
print(
"Maybe you are creating an agent for test purposes. I'll try to load the model from a different path"
)
agent_nnet.load("../../sub_watten/training/default_nn/best.h5")
return agent_nnet
class SubWattenBaggingModel(Agent):
def __init__(self, game):
super().__init__(name="SUB WATTEN BAGGING AGENT")
self.game = game
x, y = game.get_observation_size()
nnet1 = SubWattenNNet(x, y, 1, game.get_action_size())
nnet2 = AsymmetricSubWattenNNet(x, y, 1, game.get_action_size())
# Setting up the symmetric and asymmetric nnet
self.agent_symmetric = AgentNNet(nnet1)
self.agent_asymmetric = AgentNNet(nnet2)
try:
self.agent_symmetric.load("games/sub_watten/training/best.h5")
self.agent_asymmetric.load(
"games/asymmetric_sub_watten/training/v2/best.h5")
except OSError:
print("File not found with games/sub_watten/training/best.h5")
print(
"Maybe you are creating an agent for test purposes. I'll try to load the model from a different path"
)
self.agent_symmetric.load("../../sub_watten/training/best.h5")
self.agent_asymmetric.load(
"../../asymmetric_sub_watten/training/v2/best.h5")
def predict(self, game: Game, game_player):
# Prediction of both nnet
actions_s, obs_value_s = self.agent_symmetric.predict(
game, game_player)
actions_a, obs_value_a = self.agent_asymmetric.predict(
game, game_player)
# Symmetric has 78,96% of success against random
# Asymmetric has 73,14% of success against random
actions = (7896 * actions_s + 7314 * actions_a) / (7896 + 7314)
obs_value = (7896 * obs_value_s + 7314 * obs_value_a) / (7896 + 7314)
return actions, obs_value