Towards Continual Reinforcement Learning: A Review and Perspectives (2012.13490v2)

Abstract: In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.

• The paper introduces a unified taxonomy for continual reinforcement learning that categorizes approaches based on the sources and drivers of non-stationarity.
• Methodologies like explicit knowledge retention and modular architectures are analyzed for mitigating catastrophic forgetting and enhancing transferability.
• The review outlines future challenges and interdisciplinary insights to advance agent adaptability in dynamic, continually evolving environments.

Towards Continual Reinforcement Learning: A Review and Perspectives

The paper "Towards Continual Reinforcement Learning: A Review and Perspectives," authored by Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup, presents a comprehensive examination of the various formulations and approaches within the domain of continual reinforcement learning (CRL), often termed lifelong or non-stationary reinforcement learning (RL). By providing a literature review, the authors aim to create a unified framework to understand and improve upon existing methodologies in the paper of agents that learn continuously from an environment where conditions and goals change over time.

A central theme of the paper is the argument that reinforcement learning is a natural candidate for modeling continual learning, due to the inherent agent-environment interaction paradigm that RL offers. The authors provide a taxonomy to categorize CRL formulations, focusing on two key properties: the scope and the driver of non-stationarity. Non-stationarity in CRL is characterized by changing agent-environment dynamics over time, which can affect states, actions, rewards, and transition functions.

Several CRL approaches are explored, focusing on explicit knowledge retention, leveraging shared structure, and learning to learn.

1. Explicit Knowledge Retention: Techniques such as parameter storage, distillation, and rehearsal-based methods are discussed as ways to stabilize learning and minimize catastrophic forgetting, where newly acquired knowledge interferes destructively with previously learned information. For instance, experience replay mechanisms are highlighted for their ability to mitigate short-term biases, though they face challenges related to data storage and off-policy learning.
2. Leveraging Shared Structure: This category emphasizes using and discovering structured representations such as modular architectures, state abstractions, skills, goals, and auxiliary tasks. These methods aim to capture and exploit commonalities across tasks for improved learning efficiency and transferability. Notably, the options framework is cited for its potential to enable hierarchical learning and planning over multiple temporal scales.
3. Learning to Learn: Within this cluster, meta-learning approaches that focus on context detection, adaptability, and exploration are considered. Techniques like Bayesian reinforcement learning and meta-optimization are explored for their ability to better prepare agents for unknown future environments. Learning to adapt is particularly vital in continually evolving environments, and meta-optimization strategies are gaining traction for improving sample efficiency.

The paper also addresses the challenges of evaluating continual RL agents, emphasizing the need for benchmarks that allow for rich, configurable non-stationary settings and robust metrics beyond average accumulated rewards. These include measures of catastrophic forgetting, transfer capacity, skill reuse and composition, and exploratory effectiveness.

Moreover, the discussion on the potential intersection between continual RL and neuroscience provides insight into how biological systems balance learning and memory processes. Insights from understanding the human brain's approach to stability-plasticity balance, intrinsic reward mechanisms, and modular learning can inform future artificial intelligence development.

In looking towards the future of CRL, the authors identify several open problems and challenges. These include understanding task specification, defining the agent-environment boundary, designing comprehensive experimental protocols, and interpreting the discoveries made by CRL agents.

This paper positions itself as not only a review of the current state of continual reinforcement learning but also a call to action for advancing the field by addressing fundamental challenges and drawing from interdisciplinary insights. As the domain of RL increasingly intersects with real-world applications, the considerations highlighted in this paper will be crucial for fostering agents capable of learning and adapting in richly dynamic environments.