CAJ | 학술논문

在大规模状态空间或者连续状态空间中，将函数近似与强化学习相结合是当前机器学习领域的一个研究热点；同时，在学习过程中如何平衡探索和利用的问题更是强化学习领域的一个研究难点。针对大规模状态空间或者连续状态空间、确定环境问题中的探索和利用的平衡问题，提出了一种基于高斯过程的近似策略迭代算法。该算法利用高斯过程对带参值函数进行建模，结合生成模型，根据贝叶斯推理，求解值函数的后验分布。在学习过程中，根据值函数的概率分布，求解动作的信息价值增益，结合值函数的期望值，选择相应的动作。在一定程度上，该算法可以解决探索和利用的平衡问题，加快算法收敛。将该算法用于经典的 Mountain Car 问题，实验结果表明，该算法收敛速度较快，收敛精度较好。
재대규모상태공간혹자련속상태공간중，장함수근사여강화학습상결합시당전궤기학습영역적일개연구열점；동시，재학습과정중여하평형탐색화이용적문제경시강화학습영역적일개연구난점。침대대규모상태공간혹자련속상태공간、학정배경문제중적탐색화이용적평형문제，제출료일충기우고사과정적근사책략질대산법。해산법이용고사과정대대삼치함수진행건모，결합생성모형，근거패협사추리，구해치함수적후험분포。재학습과정중，근거치함수적개솔분포，구해동작적신식개치증익，결합치함수적기망치，선택상응적동작。재일정정도상，해산법가이해결탐색화이용적평형문제，가쾌산법수렴。장해산법용우경전적 Mountain Car 문제，실험결과표명，해산법수렴속도교쾌，수렴정도교호。
In machine learning with large or continuous state space, it is a hot topic to combine the function approximation and reinforcement learning. The study also faces a very difficult problem of how to balance the exploration and exploitation in reinforcement learning. In allusion to the exploration and exploitation dilemma in the large or continuous state space, this paper presents a novel policy iteration algorithm based on Gaussian process in deterministic environment. The algorithm uses Gaussian process to model the action-value function, and in conjunction with generative model, obtains the posteriori distribution of the parameter vector of the action-value function by Bayesian inference. During the learning process, it computes the value of perfect information according to the posteriori distribution, and then selects the appropriate action with respect to the expected value of the action-value function. The algorithm achieves the balance between exploration and exploitation to certain extent, and therefore accelerates the convergence. The experimental results on the Mountain Car problem show that the algorithm has faster convergence rate and better convergence performance.