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QTables.py
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QTables.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 19 22:55:35 2020
@author: jingci
"""
import numpy as np
import pandas as pd
import pickle
from RLBrain import RLBrain
'''
Q-learning models for learning the position of target
'''
class QLearningTable1(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'q_table1.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
#The q_table after path training
f = open(RLBrain.FILEPATH + 'path_qtable1.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
class QLearningTable2(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'q_table2.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
#The q_table after path training
f = open(RLBrain.FILEPATH + 'path_qtable2.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
class QLearningTable3(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'q_table3.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
#The q_table after path training
f = open(RLBrain.FILEPATH + 'path_qtable3.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
'''
Q-learning models for learning the position of starting point
'''
class ReturnQLearningTable1(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'Return_q_table1.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
#The q_table after path training
f = open(RLBrain.FILEPATH + 'Return_q_table1.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
class ReturnQLearningTable2(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'Return_table2.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
#The q_table after path training
f = open(RLBrain.FILEPATH + 'Return_qtable2.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
class ReturnQLearningTable3(RLBrain):
def __init__(self, actions, state):
self.actions = actions # a list
if state == "RAW":
#The q_table without previous knowledge
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
elif state == "MAP":
#The q_table after map training
f = open(RLBrain.FILEPATH + 'Return_table3.txt', 'rb')
self.q_table = pickle.load(f)
f.close()
elif state == "PATH":
f = open(RLBrain.FILEPATH + 'Return_qtable3.txt', 'rb')
self.q_table = pickle.load(f)
f.close()