Markov Models
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples Hao Sun
We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high
Near-OptimalReinforcementLearningwithSelf-Play
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn theoptimal policy by playing againstitself without any direct supervision.
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy.
AUnifyingViewofOptimisminEpisodic ReinforcementLearning
A finite episodic Markov decision process (MDP) is a tuple (S,A,H,ฮฑ,P,r) where S and A are the finite sets of states and actions withS = |S|,A = |A|, H is the (fixed) episode length andฮฑ is the initial state distribution.
