from py4j.java_gateway import JavaGateway gateway = JavaGateway() #random = gateway.jvm.java.util.Random() # create a java.util.Random instance #number1 = random.nextInt(10) gateway.innitGame() state = gateway.getState() print(state[0])
def main():
steps_until_reset = TARGET_UPDATE_FREQ
random_action_probability = INITIAL_RANDOM_ACTION
# Initialize replay memory D to capacity N
replay = ReplayBuffer(REPLAY_MEMORY_SIZE)
# Initialize action-value model with random weights
action_model = get_model()
# Initialize target model with same weights
#target_model = get_model()
#target_model.set_weights(action_model.get_weights())
env = JavaGateway()
jvm = env.jvm
for episode in range(NUM_EPISODES):
playerNumber = env.innitGame()
jObservation = env.getState()
valueSum = 0
wasNotBadMove = True
observation = []
observation.append(1)
for idx in range(9):
observation.append(jObservation[idx])
#print(observation)
done = False
reward = 0
for iteration in range(MAX_ITERATIONS):
random_action_probability *= RANDOM_ACTION_DECAY
random_action_probability = max(random_action_probability, 0.1)
old_observation = observation
# if episode % 10 == 0:
# env.render()
if np.random.random() < random_action_probability:
action = np.random.choice(range(ACTIONS_DIM))
if episode >= 10000 and playerNumber == 2:
print(old_observation)
print(valueSum)
action = np.int64(input("Space?"))
else:
q_values = get_q(action_model, observation)
action = np.argmax(q_values)
if episode > 10000 and playerNumber == 2:
print(old_observation)
print(valueSum)
action = np.int64(input("Space?"))
l = jvm.java.util.ArrayList()
l.append(playerNumber)
l.append(action.item())
reward = env.step(l)
valueSum += reward
wasNotBadMove = True
if reward == -2:
wasNotBadMove = False
#Toggle Player Number
if wasNotBadMove:
if playerNumber == 1:
playerNumber = 2
else:
playerNumber = 1
#print(wasNotBadMove)
#print(playerNumber)
iObservation = env.getState()
observation = []
observation.append(playerNumber)
for idx in range(9):
observation.append(iObservation[idx])
done = env.isDone()
if done:
# print action_model.get_weights()
# print target_model.get_weights()
#print 'Game finished after {} iterations'.format(iteration)
#reward = -200
print(observation)
print(valueSum)
replay.add(old_observation, action, reward, None)
if reward == 0:
print("good game")
if reward == 5:
modOb = old_observation
modOb[0] = playerNumber
replay.add(modOb, action, -50, None)
break
replay.add(old_observation, action, reward, observation)
if replay.size() >= MINIBATCH_SIZE:
sample_transitions = replay.sample(MINIBATCH_SIZE)
update_action(action_model, action_model, sample_transitions)
steps_until_reset -= 1
