Python car_and_reward Beispiele

Beispiel #1

Datei: ex4_5.py Projekt: KDmike123/Reinforcement-Learning-An-introduction

def policy_improv():
	policy_stable = True
	for i in xrange(max_car+1):
		for j in xrange(max_car+1):
			temp = pi[i, j]
			rewardlist = np.zeros(11)
			index = 0
			for k in xrange(-max_move, max_move+1):
				rewardlist[index] = car_and_reward(i, j, k, V)
				index += 1
			pi[i, j] = np.argmax(rewardlist)-5
			if temp != pi[i, j]:
				policy_stable = False
	return policy_stable

Beispiel #2

def policy_improv():
    policy_stable = True
    for i in xrange(max_car + 1):
        for j in xrange(max_car + 1):
            temp = pi[i, j]
            rewardlist = np.zeros(11)
            index = 0
            for k in xrange(-max_move, max_move + 1):
                rewardlist[index] = car_and_reward(i, j, k, V)
                index += 1
            pi[i, j] = np.argmax(rewardlist) - 5
            if temp != pi[i, j]:
                policy_stable = False
    return policy_stable

Beispiel #3

Datei: ex4_5.py Projekt: KDmike123/Reinforcement-Learning-An-introduction

def policy_eval():
	while True:
		delta = 0
		for i in xrange(max_car+1):
			for j in xrange(max_car+1):
				# print i,j
				temp = V[i, j]
				V[i, j] = car_and_reward(i, j, pi[i, j], V)
				delta = max(delta, abs(temp-V[i, j]))
				# print V
				# raw_input('Enter')
		# print V
		# raw_input('enter')
		if delta < 1e-9:
			break

Beispiel #4

def policy_eval():
    while True:
        delta = 0
        for i in xrange(max_car + 1):
            for j in xrange(max_car + 1):
                # print i,j
                temp = V[i, j]
                V[i, j] = car_and_reward(i, j, pi[i, j], V)
                delta = max(delta, abs(temp - V[i, j]))
                # print V
                # raw_input('Enter')
        # print V
        # raw_input('enter')
        if delta < 1e-9:
            break