Robust Gymnasium：ロバスト強化学習のための統合モジュラー型ベンチマーク
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
ロバスト強化学習の標準ベンチマークが不足している問題に対し、エージェントの観測・報酬・行動・環境の多様な撹乱をサポートする60以上のタスク環境を備えた統合ベンチマークを提案し、既存手法を評価した。
著者: Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas Spanos
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions. Despite the existence of a large number of RL benchmarks, there is a lack of standardized benchmarks for robust RL. Current robust RL policies often focus on a specific type of uncertainty and are evaluated in distinct, one-off environments. In this work, we introduce Robust-Gymnasium, a unified modular benchmark designed for robust RL that supports a wide variety of disruptions across all key RL components-agents' observed state and reward, agents' actions, and the environment. Offering over sixty diverse task environments spanning control and robotics, safe RL, and multi-agent RL, it provides an open-source and user-friendly tool for the community to assess current methods and foster the development of robust RL algorithms. In addition, we benchmark existing standard and robust RL algorithms within this framework, uncovering significant deficiencies in each and offering new insights.
