q_learning.py

from __future__ import print_function
import numpy as np 
import pandas as pd 
import time


np.random.seed(3)

N_STATES = 6   #length
ACTIONS = ['left','right']  #available actions
EPSILON = 0.9    #greedy police
ALPHA = 0.5   #learning rate
LAMBDA = 0.9   #discount factor
MAX_EPISODES = 13  #maximum episodes
FRESH_TIME = 0.1   #fresh time one move

def build_q_table(n_states,actions):
    table = pd.DataFrame(
        np.zeros((n_states,len(actions))),
        columns = actions,
    )
    print(table)
    return table

#build_q_table(N_STATES,ACTIONS)

def choose_action(state,q_table):
    state_actions=q_table.iloc[state,:]
    if(np.random.uniform()>EPSILON) or (state_actions.all()==0):
        action_name = np.random.choice(ACTIONS)
    else:
        action_name = state_actions.argmax()
    return action_name

def get_env_feedback(S,A):
    if A == 'right':
        if S == N_STATES-2:
            S_ = 'terminal'
            R = 1
        else:
            S_ = S + 1
            R = 0

    else:
        R = 0
        if S == 0:
            S_ = S
        else:
            S_ = S-1
    return S_, R

def update_env(S,episode,step_counter):
    env_list = ['-']*(N_STATES-1) + ['T']
    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():
    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)
            #print(1)
            q_predict = q_table.ix[S,A]
            #print(1)
            if S_ != 'terminal':
                
                q_target = R +LAMBDA*q_table.iloc[S_,:].max()
            else:
                q_target = R
                is_terminated = True
            q_table.ix[S,A]+=ALPHA * (q_target - q_predict)
            S = S_

            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)