Continual Knowledge Adaptation for Reinforcement Learning

2025-10-22

AI Summary

To address catastrophic forgetting and inefficient knowledge utilization in continual reinforcement learning (CRL) under non-stationary environments, this paper proposes the Continual Knowledge Adaptation framework for RL (CKA-RL). Methodologically, CKA-RL constructs a task-specific knowledge vector pool and employs gradient-based analysis to identify critical parameters, enabling parameter-level knowledge preservation and selective transfer. It further introduces a dynamic knowledge matching and adaptive fusion mechanism that balances storage efficiency with retention of essential information. The framework supports efficient accumulation, reuse, and cross-task transfer of historical knowledge. Extensive experiments on three standard benchmarks demonstrate that CKA-RL significantly outperforms existing state-of-the-art methods: it achieves a 4.20% improvement in overall performance and an 8.02% gain in forward transfer, thereby enhancing scalability and generalization capability in continual RL.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Learning & Optimization for ROB
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Reinforcement Learning enables agents to learn optimal behaviors through interactions with environments. However, real-world environments are typically non-stationary, requiring agents to continuously adapt to new tasks and changing conditions. Although Continual Reinforcement Learning facilitates learning across multiple tasks, existing methods often suffer from catastrophic forgetting and inefficient knowledge utilization. To address these challenges, we propose Continual Knowledge Adaptation for Reinforcement Learning (CKA-RL), which enables the accumulation and effective utilization of historical knowledge. Specifically, we introduce a Continual Knowledge Adaptation strategy, which involves maintaining a task-specific knowledge vector pool and dynamically using historical knowledge to adapt the agent to new tasks. This process mitigates catastrophic forgetting and enables efficient knowledge transfer across tasks by preserving and adapting critical model parameters. Additionally, we propose an Adaptive Knowledge Merging mechanism that combines similar knowledge vectors to address scalability challenges, reducing memory requirements while ensuring the retention of essential knowledge. Experiments on three benchmarks demonstrate that the proposed CKA-RL outperforms state-of-the-art methods, achieving an improvement of 4.20% in overall performance and 8.02% in forward transfer. The source code is available at https://github.com/Fhujinwu/CKA-RL.

Problem

• Addresses catastrophic forgetting in non-stationary reinforcement learning environments
• Enables efficient knowledge transfer across sequential tasks in RL
• Reduces memory requirements while preserving essential historical knowledge

Innovation

• Maintains task-specific knowledge vector pool
• Dynamically adapts historical knowledge to new tasks
• Merges similar knowledge vectors for scalability