Detecting Silent Faults at the Environment-Trainer Boundary in Reinforcement Learning
Overview
This research introduces a method to detect silent faults occurring at the boundary between reinforcement learning trainers and external environments. By implementing a step ledger on both sides of the wire protocol, the authors demonstrate that their approach can catch and locate every injected fault across various robotics benchmarks (Pendulum, HalfCheetah, Hopper, Pusher) with minimal overhead (median 3% more training time). The study evaluates up to 23 types of faults in Gymnasium 1.3 and other multi-agent libraries, recommending specific logging practices for robust evaluation.
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Reported quantity · faults injected into wire protocol: 23 other · Basis not reported
“arXiv:2611.08144v1 Announce Type: new Abstract: Robot reinforcement learning increasingly runs environments outside the trainer: in another process, on another machine, in another language. Many faults at this boundary raise no error. We make the boundary a wire protocol and ask what notices a fault in it. On Pendulum, HalfCheetah, Hopper and a 7-joint arm (Pusher), we injected up to 23 faults into what crosses the w”
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- arXiv Robotics — research abstractsSignalDetecting Silent Faults at the Environment-Trainer Boundary in Reinforcement Learning
This research introduces a method to detect silent faults occurring at the boundary between reinforcement learning trainers and external environments. By implementing a step ledger on both sides of the wire protocol, the authors demonstrate that their approach can catch and locate every injected fault across various robotics benchmarks (Pendulum, HalfCheetah, Hopper, Pusher) with minimal overhead (median 3% more training time). The study evaluates up to 23 types of faults in Gymnasium 1.3 and other multi-agent libraries, recommending specific logging practices for robust evaluation.
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