""" A simple example for Reinforcement Learning using table lookup Q-learning method. An agent "o" is on the left of a 1 dimensional world, the treasure is on the rightmost location. Run this program and to see how the agent will improve its strategy of finding the treasure. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ import numpy as np import pandas as pd import time #random会生成相同的随机数 np.random.seed(2) # reproducible N_STATES = 6 # the length of the 1 dimensional world ACTIONS = ['left', 'right'] # available actions EPSILON = 0.9 # greedy police ALPHA = 0.1 # learning rate GAMMA = 0.9 # discount factor MAX_EPISODES = 13 # maximum episodes FRESH_TIME = 0.3 # fresh time for one move def build_q_table(n_states, actions): #生成q_table 表 6行2列 并赋值为0,两列叫left 和right table = pd.DataFrame( np.zeros((n_states, len(actions))), # q_table initial values columns=actions, # actions's name ) # print(table) # show table return table def choose_action(state, q_table): # This is how to choose an action state_actions = q_table.iloc[state, :]#获取q_table表中 某一行的state 的值 #print('\r') #print(state_actions) #随机生成【0,1】间的随机数>EPSILON(以10%的概率随机)或state_actions全为0时随机 if (np.random.uniform() > EPSILON) or ((state_actions == 0).all()): # act non-greedy or state-action have no value #从ACTIONS随机选一个 action_name = np.random.choice(ACTIONS) print(' choice random:' + action_name) print(state_actions) else: # act greedy #返回最大的数值的索引 action_name = state_actions.idxmax() # replace argmax to idxmax as argmax means a different function in newer version of pandas print('choice maxindex:'+action_name) print(state_actions) return action_name def get_env_feedback(S, A): #输入当前状态S 和 动作 A #返回下一个状态S_和动作后的奖赏 # This is how agent will interact with the environment if A == 'right': # move right if S == N_STATES - 2: # terminate S_ = 'terminal' R = 1 else: S_ = S + 1 R = 0 else: # move left R = 0 if S == 0: S_ = S # reach the wall else: S_ = S - 1 return S_, R def update_env(S, episode, step_counter): # This is how environment be updated env_list = ['-']*(N_STATES-1) + ['T'] # '---------T' our environment if S == 'terminal': interaction = 'Episode %s: total_steps = %s' % (episode+1, step_counter) print('\r{}'.format(interaction), end='') time.sleep(2) print('\r ', end='') else: env_list[S] = 'o' interaction = ''.join(env_list) print('\r{}'.format(interaction), end='') time.sleep(FRESH_TIME) def rl(): # main part of RL loop q_table = build_q_table(N_STATES, ACTIONS) for episode in range(MAX_EPISODES): step_counter = 0 S = 0 is_terminated = False update_env(S, episode, step_counter) while not is_terminated: A = choose_action(S, q_table) S_, R = get_env_feedback(S, A) # take action & get next state and reward q_predict = q_table.loc[S, A]#表格中动作的预测奖赏 if S_ != 'terminal': #动作后的实际奖赏+GAMMA*下一步的预测值(S_时的动作奖赏)的最大子 q_target = R + GAMMA * q_table.iloc[S_, :].max() # next state is not terminal else: q_target = R # next state is terminal is_terminated = True # terminate this episode q_table.loc[S, A] += ALPHA * (q_target - q_predict) # update q_table 向q_target靠近 S = S_ # move to next state update_env(S, episode, step_counter+1)#更新环境 step_counter += 1 return q_table if __name__ == "__main__": q_table = rl() print('\r\nQ-table:\n') print(q_table)
sarsa只需要改动两条 action_ = RL.choose_action(str(observation_))#由随机产生a_ 变成实际选出并做动作 q_target = r + self.gamma * self.q_table.loc[s_, a_]