AI RESEARCH
JAXenstein: Accelerated Benchmarking for First-Person Environments
arXiv CS.LG
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ArXi:2605.19926v1 Announce Type: new The progression of reinforcement learning algorithms have been driven by challenging benchmarks. The rate in which a researcher can iterate on a problem setting directly impacts the speed of algorithm development. Modern machine learning has produced tools that allow for fast and scalable algorithm development like the JAX library. With the availability of these tools, a serious bottleneck in algorithm development is the availability of large and complex domains for experimentation.