Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
It provides a comprehensive overview for researchers and practitioners, but it is incremental as it synthesizes existing work without introducing new methods or results.
This paper reviews the field of continual reinforcement learning, addressing the problem of enabling RL agents to learn sequentially and continuously across diverse tasks, with a focus on recent advancements in robotics and evaluation environments.
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuous reinforcement learning. This survey reviews how continual learning transforms RL agents into dynamic continual learners. This enables RL agents to acquire and retain useful and reusable knowledge seamlessly. The paper delves into fundamental aspects of continual reinforcement learning, exploring key concepts, significant challenges, and novel methodologies. Special emphasis is placed on recent advancements in continual reinforcement learning within robotics, along with a succinct overview of evaluation environments utilized in prominent research, facilitating accessibility for newcomers to the field. The review concludes with a discussion on limitations and promising future directions, providing valuable insights for researchers and practitioners alike.