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most citedPolicy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

2 citations · 2 across the 6 of their papers we have counts for

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cs.LG2021

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning

Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima +2

Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementatio…

cs.LG2021★ 2 cited

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno +4

Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In…