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Found 4 papers, 1 papers with code
On the Convergence of Continual Learning with Adaptive Methods
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 conceptualization of the plasticity-stability dilemma.
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
Alleviating overestimation bias is a critical challenge for deep reinforcement learning to achieve successful performance on more complex tasks or offline datasets containing out-of-distribution data.
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty.
Distributional Reinforcement Learning
reinforcement-learning
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks.
