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stat.ML2020
Identifying Critical States by the Action-Based Variance of Expected Return
Izumi Karino, Yoshiyuki Ohmura, Yasuo Kuniyoshi
The balance of exploration and exploitation plays a crucial role in accelerating reinforcement learning (RL). To deploy an RL agent in human society, its explainability is also ess…
stat.ML2018
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning
Izumi Karino, Kazutoshi Tanaka, Ryuma Niiyama +1
This paper proposes an exploration method for deep reinforcement learning based on parameter space noise. Recent studies have experimentally shown that parameter space noise result…