1 citations · 2 across the 11 of their papers we have counts for
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Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
Taehyun Cho, Seungyub Han, Seokhun Ju +3
Distributional reinforcement learning improves performance by capturing environmental stochasticity, but a comprehensive theoretical understanding of its effectiveness remains elus…
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
Dohyeong Kim, Taehyun Cho, Seungyub Han +3
The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-base…
On the Convergence of Continual Learning with Adaptive Methods
Seungyub Han, Yeongmo Kim, Taehyun Cho +1
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the concep…
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
Dohyeok Lee, Seungyub Han, Taehyun Cho +1
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-o…