1 citations · 1 across the 4 of their papers we have counts for
5 papers
Thompson Sampling for Learning in Zero-Sum Games
Kenshi Abe, Kaito Ariu, Po-An Wang
Thompson sampling (TS) achieves logarithmic and asymptotically optimal regret in classical stochastic bandits, but its behavior in multi-agent learning remains less understood. We…
Policy Testing in Markov Decision Processes
Kaito Ariu, Po-An Wang, Alexandre Proutiere +1
We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model with static sampling. The goal is to dec…
Evaluation of Best-of-N Sampling Strategies for Language Model Alignment
Yuki Ichihara, Yuu Jinnai, Tetsuro Morimura +4
Best-of-N (BoN) sampling with a reward model has been shown to be an effective strategy for aligning Large Language Models (LLMs) with human preferences at the time of decoding. Bo…
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes
Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe +1
Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. While PG can work well even…
Mean-Variance Efficient Reinforcement Learning with Applications to Dynamic Financial Investment
Masahiro Kato, Kei Nakagawa, Kenshi Abe +2
This study investigates the mean-variance (MV) trade-off in reinforcement learning (RL), an instance of the sequential decision-making under uncertainty. Our objective is to obtain…