2000 character limit reached
Reinforcement Learning: Stochastic Approximation Algorithms for Markov Decision Processes
Published 23 Dec 2015 in math.OC | (1512.07669v1)
Abstract: This article presents a short and concise description of stochastic approximation algorithms in reinforcement learning of Markov decision processes. The algorithms can also be used as a suboptimal method for partially observed Markov decision processes.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.