%0 Journal Article
%T Algorithm of stable state spaces in reinforcement learning<br>强化学习算法的稳定状态空间控制
%A ZHENG Yu
%A LUO Si-wei
%A LV Zi-ang
%A <br>郑宇
%A 罗四维
%A 吕子昂
%J 计算机应用
%D 2008
%I 
%X Reinforcement leaning often suffers from the fact that the number of trials grows exponentially as the state spaces expand. This paper proposed an algorithm of stable state spaces in reinforcement learning to overcome this problem. The algorithm aimed for optimal actions in stable state spaces and focused exploration areas on stable state spaces instead of the whole state spaces. As stable state spaces is only a small fraction of the whole state spaces, the number of trials in our algorithm does not grow exponentially as the state spaces expand.
%K reinforcement learning
%K Markov Decision Process (MDP)
%K stable state
%K inverted pendulum<br>强化学习
%K 马尔可夫决策过程
%K 稳定状态
%K 倒立摆
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=DCD62ABCB686A998D33FD276792C97B2&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=94C357A881DFC066&sid=F016DE22306D4D4A&eid=DF8B97D5075E2D12&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=10