from game import Game
-learning_rate = 0.1
+learning_rate = 1.0
discount_factor = 1.0
states_dim = 36864 # 2^10 * 6^2
opt_qid = find_option_qid(opt)
opt_qid_pairs.append( [opt, opt_qid] )
opt_qsum += qs[opt_qid]
- #random.shuffle(opt_qid_pairs)
ran_pt = random.uniform(0.0, opt_qsum)
decision_pt = 0.0
for opt_qid_pair in opt_qid_pairs:
+ discount_factor * np.max(Q[new_state_qid, :])
- Q[state_qid, opt_qid])
state_qid = new_state_qid
- Q[state_qid, opt_qid] = 0
+ Q[state_qid, :] = 0
running_score[0] *= 0.99999999
running_score[0] += g.get_score()
running_score[1] *= 0.99999999