Continual Knowledge Adaptation For Reinforcement Learning

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, w

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyhu2025continual

Related papers

• Task-agnostic Continual Reinforcement Learning: Gaining Insights And Overcoming Challenges (2022)0.00
• Demonstration-guided Continual Reinforcement Learning In Dynamic Environments (2025)0.00
• Advancements And Challenges In Continual Reinforcement Learning: A Comprehensive Review (2025)0.00
• Continual Reinforcement Learning By Planning With Online World Models (2025)0.00
• Experience Replay For Continual Learning (2018)0.00
• Multi-granularity Knowledge Transfer For Continual Reinforcement Learning (2024)2.26
• Safe Continual Reinforcement Learning In Non-stationary Environments (2026)12.89
• Improving Generalization To New Environments And Removing Catastrophic Forgetting In Reinforcement Learning By Using An Eco-system Of Agents (2022)0.00