class DeepQLearner (BaseLearner):
def __init__(self):
super(DeepQLearner, self).__init__()
self.discount_rate = 0.95
self.network = QNetwork()
self.database = ExperienceDatabase(100000)
self.weights = []
def decide_action(self, new_state, possible_moves):
# compute Q-scores with forward-propagation
scores = np.zeros(len(possible_moves))
for i, move in enumerate(possible_moves):
scores[i] = self.network.use_model(new_state, move)
# train neural-network
if self.last_state is not None:
self.database.add(self.last_state, self.last_action, self.last_reward, new_state, possible_moves)
past_last_state, past_last_action, past_last_reward, past_new_state, past_possible_moves = self.database.sample(1)[0]
best_score = None
for move in past_possible_moves:
best_score = max(best_score, self.network.use_model(past_new_state, move))
# update weights with back-propagation
self.network.update_model(past_last_state, past_last_action, float(past_last_reward + self.discount_rate * best_score))
return possible_moves[self.explorer.decide_action(self.epoch, scores)]
def end_epoch(self, score):
super(DeepQLearner, self).end_epoch(score)
#save the network weights at this epoch
if self.epoch % 1000 == 0:
self.weights.append(self.network.get_all_weights())
def __init__(self):
super(DeepQLearner, self).__init__()
self.discount_rate = 0.95
self.network = QNetwork()
self.database = ExperienceDatabase(100000)
self.weights = []
