from game import Game
-learning_rate = 1.0
+learning_rate = 0.001
discount_factor = 1.0
states_dim = 36864 # 2^10 * 6^2
old_score = g.get_score()
g.shut(opt)
g.dice()
- reward = (g.get_score() - old_score) / 11.0
+ reward = g.get_score() - old_score
new_state_qid = find_state_qid(g.get_shutable(), g.get_diced())
Q[state_qid, opt_qid] += \
learning_rate * (reward
+ discount_factor * np.max(Q[new_state_qid, :])
- Q[state_qid, opt_qid])
state_qid = new_state_qid
- Q[state_qid, :] = 0
+ Q[state_qid, :] = 0.0
running_score[0] *= 0.99999999
running_score[0] += g.get_score()
running_score[1] *= 0.99999999