OpenNovelty.org

Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
continual learningreinforcement learningmeta learning
Abstract:

Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual-learner framework comprising a fast learner and a meta learner to address continual Reinforcement Learning~(RL) problems. These two learners are coupled to perform distinct yet complementary roles: the fast learner focuses on knowledge transfer, while the meta learner ensures knowledge integration. In contrast to traditional multi-task RL approaches that share knowledge through average return maximization, our meta learner incrementally integrates new experiences by explicitly minimizing catastrophic forgetting, thereby supporting efficient cumulative knowledge transfer for the fast learner. To facilitate rapid adaptation in new environments, we introduce an adaptive meta warm-up mechanism that selectively harnesses past knowledge. We conduct experiments in various pixel-based and continuous control benchmarks, revealing the superior performance of continual learning for our proposed dual-learner approach relative to baseline methods.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a dual-learner framework for continual reinforcement learning, comprising a fast learner for knowledge transfer and a meta learner for knowledge integration. It resides in the 'Dual-Learner and Meta-Learning Frameworks' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Bidirectional Knowledge Transfer and Integration', suggesting the work addresses both forward adaptation and backward consolidation rather than unidirectional transfer alone.

The taxonomy reveals neighboring research directions that share conceptual overlap but differ in mechanism. The sibling leaf 'Backward Knowledge Transfer and Refinement' focuses explicitly on improving old tasks using new knowledge, while 'Knowledge Consolidation and Retention' emphasizes long-term stability. Adjacent branches include 'Modular and Compositional Knowledge Structures' (five papers) and 'Experience Replay and Generative Rehearsal' (three papers), which tackle knowledge reuse through architectural modularity or memory-based rehearsal rather than dual-learner meta-optimization. The paper's approach diverges by coupling two learners with distinct roles rather than relying on single-agent architectures or replay buffers.

Among the three contributions analyzed, the dual-learner framework examined ten candidates and found one refutable prior work, suggesting moderate novelty within the limited search scope. The catastrophic forgetting measure examined three candidates with one refutable match, indicating some conceptual overlap in how forgetting is quantified. The adaptive meta warm-up mechanism examined three candidates with zero refutations, appearing more distinctive among the sixteen total candidates reviewed. These statistics reflect a top-K semantic search, not an exhaustive survey, so unexamined literature may contain additional overlapping work.

Based on the limited search scope of sixteen candidates, the dual-learner architecture and adaptive warm-up mechanism appear to offer incremental advances over existing meta-learning frameworks, though the catastrophic forgetting formulation shows closer ties to prior work. The sparse population of the target taxonomy leaf (three papers) suggests this specific combination of fast and meta learners remains relatively underexplored, but the analysis cannot rule out relevant work outside the top-K semantic matches examined.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: continual reinforcement learning with knowledge transfer and integration. The field addresses how agents can learn sequentially across multiple tasks while retaining and reusing prior knowledge. The taxonomy reveals several complementary research directions: Knowledge Transfer Mechanisms and Architectures explores how to share representations and policies across tasks, often through modular or compositional designs; Catastrophic Forgetting Prevention and Memory Management focuses on preserving past knowledge through replay buffers, regularization, or architectural isolation; Bidirectional Knowledge Transfer and Integration examines not only forward transfer to new tasks but also backward refinement of earlier skills; Adaptive Task Sequencing and Dynamic Environment Handling considers curriculum design and non-stationary settings; Domain-Specific Continual RL Applications demonstrates these ideas in robotics, dialogue systems, and other practical domains; and Theoretical Foundations and Benchmarking provides formal guarantees and standardized evaluation protocols such as Libero Benchmark[3]. Representative works like Continual RL Survey[7] and Reuse Compose Survey[27] synthesize these themes, while methods such as Modulating Masks[6] and Mixture Progressive Experts[5] illustrate architectural strategies for balancing plasticity and stability. A particularly active line of work centers on dual-learner and meta-learning frameworks that maintain separate fast and slow learning components or leverage meta-optimization to accelerate adaptation. Fast Meta Learners[0] exemplifies this approach by combining rapid task-specific learning with meta-level knowledge consolidation, enabling efficient forward transfer while mitigating interference. This contrasts with methods like Similarity-Driven Weighting[13], which dynamically compose prior policies based on task similarity, and Dynamic Retrieval Expert[17], which selectively retrieves relevant past experiences. Whereas Similarity-Driven Weighting[13] emphasizes explicit policy reuse and Dynamic Retrieval Expert[17] focuses on memory-based retrieval, Fast Meta Learners[0] integrates meta-learning to achieve faster convergence on novel tasks while preserving backward compatibility. These complementary strategies highlight ongoing trade-offs between computational efficiency, sample complexity, and the degree of architectural specialization required to balance continual learning objectives.

Claimed Contributions

New foundations for continual RL: MDP difference and catastrophic forgetting measure

The authors introduce formal definitions to quantify environment similarity (MDP difference) and catastrophic forgetting applicable to both value-based and policy-based RL. These foundations provide a principled basis for understanding when knowledge transfer is beneficial and how to mitigate forgetting in continual RL.

3 retrieved papers
Can Refute
Dual-learner framework with fast and meta learners

The authors propose a dual-learner architecture inspired by hippocampal-cortical interactions in the brain. The fast learner rapidly adapts to new tasks through knowledge transfer, while the meta learner consolidates experiences through knowledge integration by minimizing catastrophic forgetting.

10 retrieved papers
Can Refute
Adaptive meta warm-up mechanism for knowledge transfer

The authors develop an adaptive meta warm-up strategy that uses a one-vs-all hypothesis test to select the most effective initialization among the meta learner, preceding fast learner, or random initialization. This mechanism addresses the negative transfer issue while enabling efficient knowledge reuse.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

New foundations for continual RL: MDP difference and catastrophic forgetting measure

The authors introduce formal definitions to quantify environment similarity (MDP difference) and catastrophic forgetting applicable to both value-based and policy-based RL. These foundations provide a principled basis for understanding when knowledge transfer is beneficial and how to mitigate forgetting in continual RL.

Contribution

Dual-learner framework with fast and meta learners

The authors propose a dual-learner architecture inspired by hippocampal-cortical interactions in the brain. The fast learner rapidly adapts to new tasks through knowledge transfer, while the meta learner consolidates experiences through knowledge integration by minimizing catastrophic forgetting.

Contribution

Adaptive meta warm-up mechanism for knowledge transfer

The authors develop an adaptive meta warm-up strategy that uses a one-vs-all hypothesis test to select the most effective initialization among the meta learner, preceding fast learner, or random initialization. This mechanism addresses the negative transfer issue while enabling efficient knowledge reuse.

Table of Contents