def qLearning(maxsteps = 100, rate_a = 0.6, rate_g = 0.3):
#- state = [board_state, tetrimino]: 盤面の状態, テトリミノ
#- steps, maxsteps: エピソード数
#- state_list: 状態リスト．盤面の状態と落ちてきている駒
#- action_list: 行動リスト．[横移動回数，回転の回数]を保持
#- Qtable: Q値を格納する二次元配列
steps = 0
flag = 0
while steps < maxsteps:
rate = change_rate(steps/maxsteps, rate_a, rate_g)
alpha, gamma = rate_a * rate, rate_g * rate
#state_list = []
pt.init(0.5)
alive = 1
old_board = board = []
print "step:", steps, "start"
while alive: #- ゲームオーバーになるまで
if flag == 0:
board = pt.getBoard()
if old_board != board:
for i in range(0,len(board)):
for j in range(0,len(board[i])):
if board[i][j] != 0 and board[i][j] != 8: board[i][j] = 1
piece = pt.getPiece()
state = [board, piece]
#- 行動の探査・搾取
action = egreedy.eGreedy(state) # softmaxでもよいが、今はとりあえず
#- state_list, action_list, Qtableの更新
if action not in lists.action_list:
lists.action_list.append(action)
add_state(state)
updateQ.updateQ(state, action, alpha, gamma)
#- actionの実行
old_board = board
flag = 1
#次の状態を準備
if flag == 1:
step = 0
while flag != 2:
flag = doaction(action, piece)
step += 1
if step % 100 == 0:
flag = 2
elif flag == 2:
pt.loop()
#pt.move('down')
pt.drop()
if pt.param.flag['update']:
pt.loop()
flag = 0
if pt.param.flag['gameover']: alive = flag = 0
steps += 1
reset()
check = 1
# if len(lists.Qtable) != len(lists.state_list): print "check"
while check:
if [] in lists.Qtable: lists.Qtable.remove([])
else: check = 0
# if len(lists.Qtable) != len(lists.state_list): print len(lists.state_list),len(lists.Qtable)
# flag = steps / maxsteps
return lists.state_list, lists.action_list, lists.Qtable
[8,8,8,8,8,8,8,8,8,8,8,8]]
b = pt.getBoard()
ans = pt.answer(pt.getBoard(),pt.getPiece()[1], pt.getPiece()[2], 0, 2)
#ans = pt.answer([2,3,4,5,4,3],pt.getPiece()[1], pt.getPiece()[2], 0, 2)
pprint(ans)
pt.rotate('ccw')
pt.rotate('ccw')
# メインループ
while(pt.alive()):
#########################
# ループごとの解析、操作をここに書く
pt.drop()
# 次のループへ
pt.loop()
step += 1
# 終了
pt.init(100)
pprint(pt.getBoard())
pt.quit()
