1 citations · 1 across the 2 of their papers we have counts for
5 papers
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning
Taisuke Kobayashi
In reinforcement learning (RL), temporal difference (TD) error is known to be related to the firing rate of dopamine neurons. It has been observed that each dopamine neuron does no…
Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error
Taisuke Kobayashi
In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the TD error is defined by a bootstrap…
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks
Taisuke Kobayashi, Shingo Murata
This paper proposes a novel stable learning theory for recurrent neural networks (RNNs), so-called variational adaptive noise and dropout (VAND). As stabilizing factors for RNNs, n…
LiRA: Light-Robust Adversary for Model-based Reinforcement Learning in Real World
Taisuke Kobayashi
Model-based reinforcement learning has attracted much attention due to its high sample efficiency and is expected to be applied to real-world robotic applications. In the real worl…
Improvements of Dark Experience Replay and Reservoir Sampling towards Better Balance between Consolidation and Plasticity
Taisuke Kobayashi
Continual learning is the one of the most essential abilities for autonomous agents, which can incrementally learn daily-life skills. For this ultimate goal, a simple but powerful…