20 citations · 32 across the 7 of their papers we have counts for
7 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…
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…
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Paavo Parmas, Carl Edward Rasmussen, Jan Peters +1
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instabil…
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…
Unbounded Output Networks for Classification
Stefan Elfwing, Eiji Uchibe, Kenji Doya
We proposed the expected energy-based restricted Boltzmann machine (EE-RBM) as a discriminative RBM method for classification. Two characteristics of the EE-RBM are that the output…