python 終端環境搭建
阿新 • • 發佈:2020-11-29
import numpy as np import pandas as pd import time 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): 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, :] if (np.random.uniform() > EPSILON) or ((state_actions == 0).all()): # act non-greedy or state-action have no value action_name = np.random.choice(ACTIONS) else: # act greedy action_name = state_actions.idxmax() # replace argmax to idxmax as argmax means a different function in newer version of pandas return action_name def get_env_feedback(S, A): # 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] if S_ != 'terminal': 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 S = S_ # move to next state update_env(S, episode, step_counter+1) step_counter += 1 return q_table if __name__ == "__main__": q_table = rl() print('\r\nQ-table:\n') print(q_table)