Agent-Arena统一评估各类机器人控制算法，支持仿真与真实机器人
Agent-Arena: A General Framework for Evaluating Control Algorithms
- 通用框架适配所有控制算法，无需重写代码
- 可无缝运行于仿真与真实机器人环境
- 助力算法快速集成与复现，节省调参时间
机器人研究本质上具有挑战性，需掌握多种环境和控制算法。将算法适配新环境常面临巨大困难，尤其在数据驱动方法中需要大量超参数调优。为此，我们提出Agent-Arena，一个用Python构建的框架，旨在简化跨多种基准环境的决策策略的集成、复现、开发与测试。与现有框架不同，Agent-Arena具备高度通用性，支持所有类型的控制算法，并可灵活应用于仿真与真实机器人场景。
原文摘要 · Abstract (English)
Robotic research is inherently challenging, requiring expertise in diverse environments and control algorithms. Adapting algorithms to new environments often poses significant difficulties, compounded by the need for extensive hyper-parameter tuning in data-driven methods. To address these challenges, we present Agent-Arena, a Python framework designed to streamline the integration, replication, development, and testing of decision-making policies across a wide range of benchmark environments. Unlike existing frameworks, Agent-Arena is uniquely generalised to support all types of control algorithms and is adaptable to both simulation and real-robot scenarios. Please see our GitHub repository https://github.com/halid1020/agent-arena-v0.
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