222 citations · 555 across the 9 of their papers we have counts for
5 papers · 1 filter
Goal-Directed Planning by Reinforcement Learning and Active Inference
Dongqi Han, Kenji Doya, Jun Tani
What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is in…
Learning Memory-Dependent Continuous Control from Demonstrations
Siqing Hou, Dongqi Han, Jun Tani
Efficient exploration has presented a long-standing challenge in reinforcement learning, especially when rewards are sparse. A developmental system can overcome this difficulty by…
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…
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…
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…