222 citations · 555 across the 9 of their papers we have counts for
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cs.LG2019★ 20 cited
Variational Recurrent Models for Solving Partially Observable Control Tasks
Dongqi Han, Kenji Doya, Jun Tani
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how…
cs.LG2019★ 2 cited
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
Tadashi Kozuno, Dongqi Han, Kenji Doya
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which pla…
cs.LG2019★ 4 cited
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Dongqi Han, Kenji Doya, Jun Tani
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has…