latencyrl

Latencyrl is a term used in reinforcement learning to describe methods and models that explicitly account for latency in decision-making and control. Latency can arise from communication delays, sensor or actuation lag, and processing time within a system. Latencyrl treats these delays not as mere nuisances but as integral aspects of the environment or as constraints on the policy. The goal is to learn policies that are robust to delays, optimize end-to-end performance under latency, and sometimes minimize total latency penalties.

Practically, latency is modeled in several ways: as delayed observations requiring partial observability, as delayed actions that take effect after a lag, or as stochastic latency drawn from a distribution. Techniques include augmenting states with delay-aware features, using recurrent neural networks to infer hidden states, delay-tolerant reinforcement learning algorithms such as Q-learning variants and actor-critic methods with latency-aware rewards, and model-based approaches like predictive control that optimize over prospective delays. Evaluation often uses end-to-end latency, throughput, and cumulative reward with latency penalties.

Applications span networked robotics, distributed control systems, remote sensing, online decision platforms, and cloud-based gaming or computation. Challenges include non-stationary or uncertain delays, accurately measuring latency, computational overhead of latency-aware models, and benchmark standardization. The field draws on elements of partially observable MDPs, robust control, and online optimization to address these difficulties.