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20202026
most citedMean-Variance Efficient Reinforcement Learning with Applications to Dynamic Financial Investment

1 citations · 1 across the 4 of their papers we have counts for

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cs.LG2022

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…

cs.LG2020★ 1 cited

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…