Q-Learning-in-Python-master.rar_Q-learning_python qlearn库_qlearn

#!/usr/bin/env python2 import QLearning import operator import csv from math import exp from random import random from numpy import cumsum from os import listdir import sys class PRQLearning: def __init__(self, \ MDP, \ Agent, \ alpha, \ gamma, \ epsilon, \ epsilonIncrement, \ K, \ H, \ gammaPRQL, tau, \ deltaTau, \ psi, \ v, \ filePath): self.MDP = MDP self.Agent = Agent self.alpha = alpha self.gamma = gamma self.epsilon = epsilon self.epsilonIncrement = epsilonIncrement self.K = K self.H = H self.gammaPRQL = gammaPRQL self.tau = tau self.deltaTau = deltaTau self.psi = psi self.v = v self.filePath = filePath self.L = self.loadPolicies() self.myQLearning = None def execute(self): # Initialize # For each state-action pair (s, a), initialize the table # entry Q_omega(s, a) to zero The W_omega from the article is # W[omega], with omega == 0, and the same applies to U_omega # TODO: inverter 2 proximas linhas L = self.loadPolicies() Q_omega, W, U = self.initializeQ_omegaWU() myQLearning = self.initializeQLearning(Q = Q_omega) self.myQLearning = myQLearning # TODO: colocar isso de log em uma funcao a parte logStuff = [] logStuffTitle = [] logStuffTitle.append('iteration') logStuffTitle.append('state') logStuffTitle.append('psi') logStuffTitle.append('randomNumber1') logStuffTitle.append('randomNumber2') logStuffTitle.append('epsilon') logStuffTitle.append('a') logStuffTitle.append('r') logStuffTitle.append('W') logStuff.append(logStuffTitle) # used for log purposes # TODO: give better names to these lists # they're gonna be collections of the values W_avg_list = [] # list with average W from each episode Ws = [] Ps = [] # list containing the P from each episode Ks = [] # list containing the chosen policy from each episode PRvsQLs = [] # contains the quantity of episodes in which policy reuse (PR) and QLearning (QL) were used output = {'W_avg_list' : W_avg_list, 'Ws' : Ws, 'Ps' : Ps, 'Ks' : Ks, 'PRvsQL' : PRvsQLs} w_avg = 0.0 # average cummulative reward received (independently of the policy used) pr = 0; ql = 0 for episode in range(self.K): # Assign to each policy the probability of being selected P = self.assignProbsToPolicies(W, self.tau) Ps.append(P) # Choose an action policy PI_k k = self.choosePolicy(P) Ks.append([k]) # Execute the learning episode k # Receive R and the updated Q function (Q_omega) if k == 0: # If PI_k == PI_omega then execute QLearning # We are going to dump Ws_dump because the QLearning # algorithm runs only once, therefore the returned Ws # contains only the R itself (appended below in Ws[]) R, Ws_dump = myQLearning.execute() ql += 1 else: # Else, use the pi-reuse strategy to reuse PI_k # chamar funcao pi_reuse() Pi_past = L[k] R = self.pi_reuse(Pi_past, 1, self.H, self.psi, self.v, logStuff, Q_omega) pr += 1 W[k] = ( (W[k] * U[k]) + R ) / ( U[k] + 1 ) Ws.append(W[:]) U[k] = U[k] + 1 # TODO: verificar isso self.myQLearning.epsilon = self.myQLearning.epsilon + \ self.myQLearning.epsilonIncrement self.tau = self.tau + self.deltaTau w_avg = ( (w_avg * episode) + R ) / ( episode + 1 ) W_avg_list.append([w_avg]) print 'pr: ', pr, 'ql: ', ql; sys.stdout.flush() PRvsQLs.append([pr, ql]) f=open(self.filePath + '/log.txt', 'w') wr = csv.writer(f, quoting=csv.QUOTE_NONNUMERIC) wr.writerows(logStuff) f.close() return output def pi_reuse(self, Pi_past, K, H, psi, v, logStuff, Q_pi_new = None): Q_pi_new = self.initializeQ_pi_new(Q_pi_new) randomNumber1 = -1 randomNumber2 = -1 epsilon = -1 W = 0 numLine = 0 for k in range(K): # Set the initial state, s, randomly self.Agent.setInitialState() #self.Agent.state = '1' psi = self.psi for h in range(1, H + 1): s = self.Agent.state logStuffLine = [] logStuffLine.append(len(logStuff) + 1) logStuffLine.append(s) # if a goal state is reached the episode ends if s in self.MDP.G: break randomNumber1 = random() if randomNumber1 <= psi: # With a probability of psi, use the policy from the library (a = Pi_past(s)) a = self.Agent.selectBestAction(s, source = 'Probabilistic Policy', Pi = Pi_past) else: # With a probability of (1 - psi), a = epsilon_greedy(PI_new(s)) randomNumber2 = random() epsilon = 1 - psi if randomNumber2 <= epsilon: # greedy a = self.Agent.selectBestAction(s, source = 'Q-Table', Q = Q_pi_new) else: #random a = self.Agent.selectRandomAction() # Execute action # TODO: modificar para r_{k, h} # Receive the next state s' and reward r_k_h s2, r = self.Agent.executeAction(a) # TODO: manter um vetor V com os maximos maxValue = -1.0 for a2 in self.MDP.A: if Q_pi_new[s2][a2] > maxValue: maxValue = Q_pi_new[s2][a2] # Update Q_pi_new(s, a), and therefore, PI_new Q_pi_new[s][a] = ((1.0 - self.alpha) * Q_pi_new[s][a]) + \ self.alpha * (r + self.gamma * maxValue) # Set psi_(h + 1) = psi_h * v psi = psi * v # accumulate reward on W W = float(W) + pow(self.gamma, h) * r # Set s = s' self.Agent.state = s2 # prepare stuff to do the log logStuffLine.append(psi) logStuffLine.append(float(randomNumber1)) logStuffLine.append(float(randomNumber2)) logStuffLine.append(epsilon) logStuffLine.append(a) logStuffLine.append(r) logStuffLine.append(W) logStuff.append(logStuffLine) # Update W according to the formula W = float(W) / float(K) return W def assignProbsToPolicies(self, W, tau): n = len(W) denominator = 0 for p in range(n): denominator = denominator + exp(tau * W[p]) P = [] for j in range(n): p = exp(tau * W[j]) / denominator P.append(p) return P def choosePolicy(self, P): P = cumsum(P) randomNumber = random() k = 0 for i in range(len(P)): # FIXME: menor estrito, pois random.random() gera numero