arXiv:2605.22142v1 Announce Type: new Abstract: Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term transfer of symbolic observations. We study this transfer process in a temporal knowledge-graph memory setting and cast it as a neuro-symbolic value-based decision problem: for each observed triple, the agent chooses whether to keep or drop it before long-term insertion. To handle variable-sized short-term buffers, we use a per-item Q-learning design with shared parameter
This paper addresses a critical challenge in reinforcement learning and AI systems: how to effectively manage and transfer information between short-term and long-term memory in dynamic, partially observable environments. The focus on knowledge graphs and neuro-symbolic methods is timely given current AI limitations.
Improving AI's ability to learn and reason by efficiently managing memory directly contributes to the development of more robust, scalable, and intelligent AI agents. This advancement could unlock new levels of autonomy and decision-making capabilities across various applications.
The proposed neuro-symbolic approach offers a novel mechanism for AI systems to selectively retain and integrate symbolic knowledge, enhancing their adaptive learning and long-term reasoning in complex scenarios. This moves beyond 'brute force' memory solutions.
- · AI developers
- · Robotics
- · Autonomous systems
- · Knowledge management platforms
- · AI systems with poor memory management
- · Brute-force data storage approaches
AI agents operating in dynamic, partially observable environments will exhibit improved long-term learning and decision-making capabilities.
Enhanced memory transfer mechanisms will lead to more efficient and less computationally expensive AI training and operation, particularly in continuous learning settings.
The ability of AI to selectively retain and integrate symbolic knowledge could accelerate the development of truly autonomous and general-purpose AI agents capable of reasoning over vast and evolving knowledge bases.
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Read at arXiv cs.LG