arXiv AI
Oct 5

How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning

The paper introduces Adaptive Mask Selection and Composition (AMSC), a method that estimates task similarity using non‑parametric Wasserstein embeddings from state‑action‑reward samples. AMSC selects and weights previously learned policies based on z‑score‑normalized sparsemax similarity scores, forming a prior for new tasks. Experiments on CT‑graph, MiniGrid, and Continual World show that AMSC improves mean performance and forward transfer while avoiding forgetting, highlighting the importance of selecting relevant prior knowledge and its layer‑specific composition.

By Saptarshi Nath, Inish M. D'Souza, Antonio Carta, Soheil Kolouri, Andrea Soltoggio
arXiv AI
Oct 31

Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting

The paper addresses the conflict between generalization and continuous adaptation in few‑shot and continual learning. It proposes a Complementary Learning System (CLS) that separates a fast Short‑Term Memory (STM) for memorizing recent inputs from a slow Long‑Term Memory (LTM) that provides robust, generalized perceptions. Experiments on image classification show the CLS model resists forgetting in domain‑incremental continual learning and outperforms other continual learners in few‑shot settings, though overall accuracy is slightly lower than some existing methods.

By David Rawlinson, Gideon Kowadlo
arXiv Machine Learning
4d ago

Before We Adapt: The Zero-Shot Generalization Bottleneck in Continual World Models

The paper investigates how model-based reinforcement learning can support continual learning by reusing prior knowledge. It introduces a contextual Markov decision process that separates behavioral and physical context, and studies zero‑shot reuse in FourRooms tasks using Dyna‑Q and TD‑MPC. The experiments show that zero‑shot reuse degrades under physical context changes and that this degradation is strongly tied to the observation representation, highlighting representation generalization as a key bottleneck.

By Amir Mesbah, Di Wu