24 citations · 26 across the 4 of their papers we have counts for
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cs.AI2023★ 1 cited
On the Expressivity of Multidimensional Markov Reward
Shuwa Miura
We consider the expressivity of Markov rewards in sequential decision making under uncertainty. We view reward functions in Markov Decision Processes (MDPs) as a means to character…
cs.LG2023★ 24 cited
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno +3
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must co…