低代码平台AgentForge让非专家也能轻松优化强化学习参数。
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design
- 通过低代码方式统一管理强化学习各参数，无需手动映射
- 支持参数单独或联合优化，在视觉任务中表现优异
- 适合认知科学等非机器学习领域研究者使用
开发强化学习（RL）智能体通常需调整大量参数，涵盖策略、奖励函数、环境及智能体内结构。由于这些参数间关系复杂，优化过程如同黑箱，对非专家尤为困难。现有优化服务（如Vizier和Optuna）虽可处理此类问题，但因需用户手动将参数映射至不同组件，操作繁琐且需理解优化流程，限制了在认知科学等领域的应用。为此，本文提出AgentForge——一个灵活的低代码平台，可对任意参数集进行优化。其仅需少量代码即可定义优化问题，并对接多种优化器。用户可独立或联合优化参数。论文在一项具有挑战性的视觉强化学习任务中评估了其性能。
原文摘要 · Abstract (English)
Developing a reinforcement learning (RL) agent often involves identifying values for numerous parameters, covering the policy, reward function, environment, and agent-internal architecture. Since these parameters are interrelated in complex ways, optimizing them is a black-box problem that proves especially challenging for nonexperts. Although existing optimization-as-a-service platforms (e.g., Vizier and Optuna) can handle such problems, they are impractical for RL systems, since the need for manual user mapping of each parameter to distinct components makes the effort cumbersome. It also requires understanding of the optimization process, limiting the systems' application beyond the machine learning field and restricting access in areas such as cognitive science, which models human decision-making. To tackle these challenges, the paper presents AgentForge, a flexible low-code platform to optimize any parameter set across an RL system. Available at https://github.com/feferna/AgentForge, it allows an optimization problem to be defined in a few lines of code and handed to any of the interfaced optimizers. With AgentForge, the user can optimize the parameters either individually or jointly. The paper presents an evaluation of its performance for a challenging vision-based RL problem.
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