def doAction(self, chaser: Agent, target: Agent, grid: GridMap,
state: State):
#状態を記録
now_st = state.getState(chaser, target)
self.st.append(now_st)
#行動の決定
action = -1
p = random.random()
if p < epsilon or (not np.any(
self.q[now_st[0]][now_st[1]][now_st[2]])):
while True:
action = random.randint(0, 3)
if state.canMoveDirection(target, grid, action):
break
else:
max_q = -100000000
for i in range(4):
if max_q < self.q[now_st[0]][now_st[1]][
now_st[2]][i] and state.canMoveDirection(
target, grid, i):
max_q = self.q[now_st[0]][now_st[1]][now_st[2]][i]
action = i
#行動を実行・記録
target.Walk(action)
self.act.append(action)
#次状態のエージェントを予測
tmp_chaser = copy.deepcopy(chaser)
tmp_target = copy.deepcopy(target)
tmp_state = copy.deepcopy(state)
tmp_target.Walk(action)
tmp_chaser.Walk(tmp_state.nextDirection(chaser, target, grid))
next_st = state.getState(tmp_chaser, tmp_target)
#Q値更新
nextMax_q = -100000000
p = random.random()
if p < epsilon or (not np.any(
self.q[next_st[0]][next_st[1]][next_st[2]])):
while True:
next_action = random.randint(0, 3)
if state.canMoveDirection(target, grid, next_action):
nextMax_q = nextMax_q = self.q[next_st[0]][next_st[1]][
next_st[2]][next_action]
break
else:
for i in range(4):
if nextMax_q < self.q[next_st[0]][next_st[1]][
next_st[2]][i] and grid.canMove(
next_st[0] + dx[i], next_st[1] + dy[i]):
nextMax_q = self.q[next_st[0]][next_st[1]][next_st[2]][i]
self.q[now_st[0]][now_st[1]][now_st[2]][action] = (1 - alpha) * self.q[
now_st[0]][now_st[1]][now_st[2]][action] + alpha * (self.getReward(
tmp_chaser, tmp_target, tmp_state) + ganma * nextMax_q)
def greedy_doAction(self, chaser:Agent, target:Agent, grid:GridMap, state:State):
now_st = state.getState(chaser, target)
action = 0
max_q = -100000000
for i in range(4):
if max_q < self.q[now_st[0]][now_st[1]][now_st[2]][i] and state.canMoveDirection(target, grid, i):
max_q = self.q[now_st[0]][now_st[1]][now_st[2]][i]
action = i
target.Walk(action)
def doAction(self, chaser:Agent, target:Agent, grid:GridMap, state:State):
#状態を記録
now_st = state.getState(chaser, target)
self.st.append(now_st)
#行動の決定
action = -1
p = random.random()
if p < epsilon or (not np.any(self.q[now_st[0]][now_st[1]][now_st[2]])):
while True:
action = random.randint(0,3)
if state.canMoveDirection(target, grid, action):
break
else:
max_q = -100000000
for i in range(4):
if max_q < self.q[now_st[0]][now_st[1]][now_st[2]][i] and state.canMoveDirection(target, grid, i):
max_q = self.q[now_st[0]][now_st[1]][now_st[2]][i]
action = i
#行動を実行・記録
target.Walk(action)
self.act.append(action)
def chaseTarget(self, chaser: Agent, target: Agent, grid: GridMap,
state: State):
dire = state.nextDirection(chaser, target, grid)
chaser.Walk(dire)
