Ergodic Risk Measures: Towards a Risk-Aware Foundation for Continual Reinforcement Learning

ArXi:2510.02945v3 Announce Type: replace Continual reinforcement learning (continual RL) seeks to formalize the notions of lifelong learning and endless adaptation in RL. In particular, the aim of continual RL is to develop RL agents that can maintain a careful balance between retaining useful information and adapting to new situations. To date, continual RL has been explored almost exclusively through the lens of risk-neutral decision-making, in which the agent aims to optimize the expected long-run performance.