Agent-Arena: A General Framework for Evaluating Control Algorithms
This provides a tool for robotic researchers to more easily evaluate and deploy control algorithms, though it is incremental as it builds on existing framework concepts.
The paper tackles the challenge of adapting control algorithms to diverse robotic environments by introducing Agent-Arena, a Python framework that streamlines integration, replication, development, and testing of policies across benchmarks, supporting all algorithm types and both simulation and real-robot scenarios.
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.