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我正在尝试使用 python 实现马尔可夫决策过程的值迭代算法。我有一个实现。但是，这给了我许多实用程序的重复值。我的转换矩阵非常稀疏。可能，这导致了问题。但是，我不太确定这个假设是否正确。我应该如何纠正这个？代码可能很粗制滥造。我对价值迭代很陌生。所以请帮助我找出我的代码存在的问题。参考代码是这样的：http ://carlo-hamalainen.net/stuff/mdpnotes/ 。我使用了 ipod_mdp.py 代码文件。这是我的实现的片段：

num_of_states = 470   #total number of states

#initialization
V1 = [0.25] * num_of_states

get_target_index = state_index[(u'48.137654',   u'11.579949')]  #each state is a location

#print "The target index is ", get_target_index

V1[get_target_index] = -100    #assigning least cost to the target state

V2 = [0.0] * num_of_states

policy = [0.0] * num_of_states

count = 0.0

while max([abs(V1[i] - V2[i]) for i in range(num_of_states)]) > 0.001:
    print max([abs(V1[i] - V2[i]) for i in range(num_of_states)])
    print count

    for s in range(num_of_states):   #for each state
        #initialize minimum action to the first action in the list
        min_action = actions_index[actions[0]]   #initialize - get the action index for the first iteration  

        min_action_cost = cost[s, actions_index[actions[0]]]  #initialize the cost

        for w in range(num_of_states):              

            if (s, state_index[actions[0]], w) in transitions:  #if this transition exists in the matrix - non-zero value
                min_action_cost += 0.9 * transitions[s, state_index[actions[0]], w] * V1[w]

            else:
                min_action_cost += 0.9 * 0.001 * V1[w]   #if not - give it a small value of 0.001 instead of 0.0

        #get the minimum action cost for the state
        for a in actions:

            this_cost = cost[s, actions_index[a]]

            for w in range(num_of_states):          

            #   if index_state[w] != 'm': 
                if (s, state_index[a], w) in transitions:
                    this_cost += 0.9 * transitions[s, state_index[a], w] * V1[w]
                else:
                    this_cost += 0.9 * 0.001 * V1[w] 

            if this_cost < min_action_cost:

                min_action = actions_index[a]
                min_action_cost = this_cost

        V2[s] = min_action_cost

        policy[s] = min_action

    V1, V2 = V2, V1    #swap

    count += 1

非常感谢。

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1 回答 1

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我不确定我是否完全理解您的代码。如果有人需要，我将把我的实现留在这里。

import numpy as np

def valueIteration(R, P, discount, threshold):
    V = np.copy(R)
    old_V = np.copy(V)
    error = float("inf")
    while error > threshold:
        old_V, V = (V, old_V)
        max_values = np.dot(P, old_V).max(axis=1)
        np.copyto(V, R + discount * max_values)
        error = np.linalg.norm(V - old_V)
    return V

S = 30
A = 4
R = np.zeros(S)
# Goal state S-1
R[S-2] = 1

P = np.random.rand(S,A,S)
# Goal state goes to dwell state
P[S-2,:,:] = 0
P[S-2,:,S-1] = 1
P[S-1,:,:] = 0
P[S-1,:,S-1] = 1
for s in range(S-2): #goal and dwell states do not need normalization
    for a in range(A):
        P[s,a,:] /= P[s,a,:].sum()
V = valueIteration(R,P,0.97,0.001)
于 2015-08-22T12:44:44.347 回答