def choose_action(self, state):
action2q = {a: self.q[(state, a)] for a in Action.get_actions()}
if random.random() < self.epsilon:
return random.choice(action2q.keys())
max_q = max(action2q.values())
best_actions = [a for a in action2q if action2q[a] == max_q]
return random.choice(best_actions)
def learn_q(self, last_state, action, reward, now_state):
max_state_value = max(
[self.q[(now_state, a)] for a in Action.get_actions()])
# 全部使用reward初始化
if (last_state, action) not in self.q:
self.q[(last_state, action)] = reward
self.q[(
last_state,
action)] += self.alpha * (reward + self.gamma * max_state_value -
self.q[(last_state, action)])