The Free Inference Dimension: Complexity Measure for Zero-Collision Navigation under Hypothesis Mixtures

arXiv:2609.17816 · cs.LG, cs.AI · Submitted 2026-09-15 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-15

Updated: 2026-09-15

Journal ref: Proceedings of BRACIS 2026. LNCS/LNAI, Springer (2026)

Code: https://github.com/anonarticle/BRACIS2026-SUBMISSION

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 9× in this paper · explained in 13 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Adaptivity — 4× in this paper · explained in 1 episode
• Generalization Bounds — 2× in this paper · explained in 2 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Learning in Structured Stackelberg Games
• RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
• Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
• Meta reinforcement learning as task inference

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