Doina discusses the importance of adapting to environmental changes in continual learning settings, highlighting the need for effective exploration strategies. She emphasizes the distinction between abrupt and gradual changes, suggesting that understanding these dynamics can enhance agent performance. Additionally, she explores the relevance of time series prediction as a framework for examining adaptation without the complexities of exploration, pointing out the potential for agents to learn from gradual shifts in data distributions.
