RobustRLlib benchmarks 17 robust reinforcement learning algorithms
Original title: RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Evaluation across 273 perturbation settings maps where current robust RL methods still fail.
RobustRLlib is an open-source library and benchmark that puts 17 representative robust online, offline and safe reinforcement learning algorithms behind one interface. Its evaluation platform covers real-world shifts plus humanoid and vision-language-action tasks. A systematic study of 273 perturbation configurations is presented as a capability map showing where methods remain effective and where robustness gaps persist.
- The library covers robust online, offline and safe RL under one interface.
- Tasks include advanced humanoid and vision-language-action settings.
- Authors report 273 perturbation configurations in the evaluation.
- The codebase is intended for reuse and for developing new robust RL methods.
From the original article
1 min read at arxiv.orgRobust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions. Existing robust RL algorithms offer a rich set of mechanisms and principles, yet two major barriers limit their broader potential: limited algorithm reusability and an unclear empirical capability boundary.
By Xi Chen · arxiv.org
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