The implementation code for Uncertainty-based Continual Learning with Adaptive Regularization (Neurips 2019)
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Updated
May 25, 2021 - Python
The implementation code for Uncertainty-based Continual Learning with Adaptive Regularization (Neurips 2019)
Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning.
Source code of the ICML24 paper "Self-Composing Policies for Scalable Continual Reinforcement Learning" (selected for oral presentation)
Agar.io for Continual Reinforcement Learning
The official implementation of Memory-efficient DQN algorithm.
implementation of "Knowledge Retention in Continual Model-Based Reinforcement Learning"
A content recommendation platform powered by LLM agents and continuously fine-tuned LoRA adapters that dynamically learn from user feedback to deliver personalized recommendations over time.
CleanRL implementation of "Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn"
Activation Function Design Sustains Plasticity in Continual Learning. Published at ICLR 2026
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