AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design

2024-10-25

arXiv.org

AI Summary

Reinforcement learning (RL) systems suffer from complex, coupled hyperparameter tuning—spanning policies, reward functions, environments, and neural architectures—posing significant barriers for non-experts (e.g., cognitive scientists) in designing effective agents. Method: We propose the first declarative, low-code optimization framework targeting full-stack RL parameters. It unifies modeling of interdependent components, enabling cross-component joint auto-tuning without requiring users to possess expertise in optimization algorithms or perform manual parameter mapping. Implemented in Python with modular, decoupled design, it integrates optimizers such as Optuna and Vizier and provides a visual configuration interface, natively supporting end-to-end optimization for vision-based RL. Contribution/Results: Evaluated on standard vision-RL benchmarks, the framework achieves substantially improved search efficiency; user code is reduced to just 3–5 lines, and researchers without ML backgrounds can independently customize sophisticated RL agents.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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 ame, 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.

Problem

• Reinforcement Learning
• Parameter Tuning
• Cognitive Science

Innovation

• AgentForge Platform
• Reinforcement Learning Optimization
• Cross-disciplinary Application