Rethinking the Foundations for Continual Reinforcement Learning (2504.08161v1)

Abstract: Algorithms and approaches for continual reinforcement learning have gained increasing attention. Much of this early progress rests on the foundations and standard practices of traditional reinforcement learning, without questioning if they are well-suited to the challenges of continual learning agents. We suggest that many core foundations of traditional RL are, in fact, antithetical to the goals of continual reinforcement learning. We enumerate four such foundations: the Markov decision process formalism, a focus on optimal policies, the expected sum of rewards as the primary evaluation metric, and episodic benchmark environments that embrace the other three foundations. Shedding such sacredly held and taught concepts is not easy. They are self-reinforcing in that each foundation depends upon and holds up the others, making it hard to rethink each in isolation. We propose an alternative set of all four foundations that are better suited to the continual learning setting. We hope to spur on others in rethinking the traditional foundations, proposing and critiquing alternatives, and developing new algorithms and approaches enabled by better-suited foundations.

Rethinking the Foundations for Continual Reinforcement Learning

The paper "Rethinking the Foundations for Continual Reinforcement Learning" by Michael Bowling and Esraa Elelimy provides a critical reassessment of the traditional frameworks underpinning reinforcement learning (RL) with an emphasis on adapting them to better suit the objectives of continual reinforcement learning (CRL). It contends that several core tenets of traditional RL are misaligned with the demands of CRL, potentially impeding progression in this emergent field.

Critique of Traditional Reinforcement Learning Foundations

The paper identifies four foundational aspects of traditional RL that are said to be incompatible with CRL:

1. Markov Decision Processes (MDPs): Traditional RL heavily relies on MDPs, assuming finite state and action spaces, and often incorporates ergodicity assumptions such as the unichain condition. While MDPs provide a robust structure for static environments, they fail to accommodate the dynamic and non-stationary conditions requisite for CRL.
2. Focus on Optimal Artifacts: Traditional RL seeks to converge on optimal policies, value functions, and features, aiming for an artifact-focused learning model with defined training and testing phases. In contrast, CRL necessitates continuous adaptation and learning, making the dichotomy between training and testing largely irrelevant.
3. Expected Sum of Rewards: The evaluation of RL agents typically relies on the expected sum of rewards, emphasizing episodic returns that presuppose stationary environments and reset conditions. CRL environments do not lend themselves to these assumptions, thereby undermining the appropriateness of this evaluation criteria.
4. Episodic Benchmarks: Prominent RL benchmarks are episodic in nature, favoring environments with clear reset conditions and optimal policies. This episodic framework does not align with CRL's non-stationary and continuous learning environment needs.

Proposed Alternative Foundations for Continual Reinforcement Learning

To realign the foundational principles of RL with the objectives of CRL, the authors propose the following alternative set of foundations:

1. History Process Formalism: The authors advocate for a more flexible formalism that does not impose the structural constraints typical of MDPs. Instead, this framework acknowledges the complexity of real-world, non-stationary environments without presupposing regularity.
2. Behavior-Driven Goals: Instead of focusing on producing artifact outputs, the aim should be to generate adaptive behavior based on past experiences, thereby aligning training and testing into a continuous cycle of learning.
3. Hindsight Rationality: As an evaluation metric, hindsight rationality prioritizes the adaptability and rationality of agent behavior in the context of environments as they evolve, eschewing the need for comparability against an idealized, non-existent optimal policy.
4. Non-Episodic Benchmarks: There is a call for developing benchmarks that eschew clear episodic resets and support environments where continuous adaptation is a necessity, thereby necessitating distinctive performance metrics to evaluate CRL systems adequately.

Implications and Future Directions

The paper's propositions encourage a reassessment of how RL systems are conceptualized, trained, and evaluated in the context of CRL. By shifting focus towards more dynamic, non-stationary, and unresettable environments, it suggests that innovation in CRL will require both novel benchmarking environments and algorithmic strategies.

The direction outlined in this paper could transform the development and evaluation of RL systems, pushing for adaptable, real-time learning capabilities well beyond traditional episodic limits. This shift could foster advancements in myriad domains requiring dynamic decision-making processes, such as robotics, real-time strategy games, and adaptive control systems.

Critically, further research must explore practical implementations of these prospective foundations, investigate how they interact with existing RL theories, and address potential limitations or challenges in their application. The future of CRL may depend on the collective ability of the research community to not only question but also rigorously test and expand on these proposed conceptual frameworks.