On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
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Abstract (arXiv)
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning. We propose solving the general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as probabilistic inference. Furthermore, we discuss online and offline methods for planning under uncertainty. In an SSP MDP, the horizon is indefinite and unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are widely used in the artificial intelligence community. Additionally, we highlight some of the differences between solving an MDP using dynamic programming approaches widely used in the artificial intelligence community and approaches used in the active inference community.
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References · 6
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
- 2020Active inference on discrete state-spaces: a synthesiscited by 91
- 2020On the Relationship Between Active Inference and Control as Inferencecited by 42
- 2020Active Inference and Behavior Trees for Reactive Action Planning and Execution in Roboticscited by 23
- 2017Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselinescited by 20
- 2020Convex Hull Monte-Carlo Tree Searchcited by 8
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