← Back to all news
arXiv AI By Luiz Carlos Castro Guedes, Edward Hermann Haeusler

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

Read the original on arXiv AI →

The paper introduces the Free Inference dimension (dFI) as a new combinatorial measure of environmental complexity for value‑mixture agents in finite meta‑reinforcement learning. It shows that dFI is smaller than the VC‑dimension and relates to the Natarajan dimension, providing PAC‑style generalization bounds. The authors also define a complementary PMS identification dimension and demonstrate that a hybrid strategy—averaging until the first collision and then selecting—achieves optimal performance, supported by grid‑world experiments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Jun 16

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

arXiv:2606. 16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model.

By Jongmin Lee, Ernest K. Ryu, Vaneet Aggarwal
reinforcement-learning
More like this →
arXiv Machine Learning
Sep 4

Robust PAC Learning of Concurrent Stochastic Games

The paper presents the first PAC learning framework for general-sum concurrent stochastic games with uncertain transitions, addressing the challenge of Nash equilibrium existence. It introduces data‑driven L¹ confidence sets over transition kernels and a robust CSG solver that computes a social‑welfare optimal ε‑NE, or provides a certificate that no exact NE exists. The algorithm achieves polynomial sample complexity under a minimum reachability condition and is validated on benchmark CSGs with near‑optimal performance.

By Angel Y. He, David Parker
benchmarks
More like this →