The paper argues retrieval cost and credit assignment in reinforcement learning share one quantity: the value of information. Estimated from archived rollouts with a learned world model, its value at an observation gives a provable lower bound on the optimal value of acquiring it, flooring its temporal-difference target.
Why it matters
For latency- and energy-limited robots, scoring an entire memory store is costly, and credit for useful retrievals must cross long sequences.
Analysis
Memory systems may be designed around information value, retrieving only when expected benefit justifies cost.
Source: arXiv
AI disclosure: this article was produced with AI assistance and reviewed for accuracy.
Top comments (0)