Doina discusses the potential of rewriting reinforcement learning without traditional Markovian assumptions, emphasizing the importance of developing agents that can adapt to changing environments. She highlights the challenges of defining sample complexity in continual learning settings, where optimal policies may not be unique. Exciting research is underway to explore mathematical limitations and effective algorithms for these evolving environments.
