Joint Agent Memory and Exploration Learning via Novelty Signals
In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retaining raw interaction histories is computationally expensive over long trajectories. While latent memory offers a solution to compress interaction histories, its training lacks reliable supervisory signals. We introduce \textbf{J}oint \textbf{A}gent \textbf{M}emory and \textbf{E}xploration \textbf{L}earning (\textbf{JAMEL}), a framework that trains agentic memory and exploration policy together through novelty-driven interaction. We observe that memory and exploration form a mutually dependent loop: sustained exp...
| Authors | Shizuo Tian, Xiaohong Weng, Rui Kong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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