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A Definition of Continual Reinforcement Learning (2307.11046v2)

Published 20 Jul 2023 in cs.LG and cs.AI

Abstract: In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on a restricted view of learning as finding a solution, rather than treating learning as endless adaptation. In contrast, continual reinforcement learning refers to the setting in which the best agents never stop learning. Despite the importance of continual reinforcement learning, the community lacks a simple definition of the problem that highlights its commitments and makes its primary concepts precise and clear. To this end, this paper is dedicated to carefully defining the continual reinforcement learning problem. We formalize the notion of agents that "never stop learning" through a new mathematical language for analyzing and cataloging agents. Using this new language, we define a continual learning agent as one that can be understood as carrying out an implicit search process indefinitely, and continual reinforcement learning as the setting in which the best agents are all continual learning agents. We provide two motivating examples, illustrating that traditional views of multi-task reinforcement learning and continual supervised learning are special cases of our definition. Collectively, these definitions and perspectives formalize many intuitive concepts at the heart of learning, and open new research pathways surrounding continual learning agents.

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Citations (46)
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Summary

  • The paper defines continual reinforcement learning by formalizing agents' perpetual adaptation with the 'generates' and 'reaches' operators.
  • It employs a mathematical framework that distinguishes between agents that settle on a policy and those that explore indefinitely through a defined agent basis.
  • The study demonstrates practical significance with examples from multi-task and switching MDP scenarios, highlighting CRL’s relevance in dynamic environments.

A Definition of Continual Reinforcement Learning: An Expert Overview

The paper "A Definition of Continual Reinforcement Learning" addresses a fundamental challenge in AI: the ability of reinforcement learning (RL) agents to continuously adapt to their environment. Traditional reinforcement learning frameworks typically emphasize the identification and cessation of learning upon finding an optimal policy. This paper contrasts this with the concept of continual reinforcement learning (CRL), where agents should theoretically engage in perpetual learning and adaptation.

Core Contributions and Definitions

The authors of the paper systematically evaluate the necessity for a precise and formal definition of CRL. They argue that the lack of clear, concise standards has hindered research and development in this field. Their proposed solution is a formal framework wherein CRL is uniquely defined by the characteristic that all optimal agents persist in their learning processes over time.

Mathematical Formalization

To articulate the concept of continual learning agents mathematically, the paper employs innovative agent operators: "generates" and "reaches". The "generates" operator permits the understanding of agent behavior as a series of switches over a set of predefined strategies or policies known as an agent basis. The "reaches" operator characterizes whether an agent eventually fixes on a strategy or continues its exploratory process indefinitely.

The primary theoretical insights are:

  • Generates: This formalism suggests that any RL agent can be seen as searching implicitly over a history-based policy space.
  • Reaches: It dichotomizes agent behavior into those that will either fixate on a single policy or continue exploring over time.

These insights are captured through mathematical tools that specify when agents can be seen as "continual learners," i.e., when they persistently engage in an implicit search over a policy space without settling on a single policy.

Practical and Theoretical Implications

The paper introduces practical examples to illustrate their definitions, specifically focusing on the multi-task RL scenario within switching Markov Decision Processes (MDPs). Here, each environment change signifies a different task, compelling the agent to continue learning optimally across shifts rather than terminating upon mastering a single task.

Additionally, the concept reverberates into continual supervised learning settings, where agents are required to adjust to non-static probability distributions over time. This extends the relevance of CRL beyond classical RL environments into broader AI applications that require adaptive learning.

Impact and Future Directions

This formalization of CRL opens new avenues for theoretical and applied research. By establishing foundational principles, it enhances the design and evaluation of learning algorithms that need sustained adaptability, an urgent requirement for real-world applications facing dynamic and unpredictable environments.

The paper's insights suggest a shift in focus from achieving fixed optima to developing algorithms capable of maintaining continual adaptability. This poses significant implications for methodologies in AI research, calling for an examination of new performance metrics and evaluation protocols based on continual optimization criteria.

Future explorations could explore further refining these frameworks, investigating specific algorithmic implementations, and expanding the applicability of CRL across various complexities of RL agents. Moreover, there is an interest in exploring the operational and computational constraints of CRL in large-scale, real-time systems.

Overall, this paper establishes a clear foundational understanding of CRL, presenting a structured approach to redefining the AI challenge of endless adaptation. It paves the way for innovative strategies in RL that prioritize flexible and sustainable agent learning behaviors.

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