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

Developing a reinforcement learning (RL) agent often involves identifying effective values for a large number of parameters, covering the policy, reward function, environment, and the agent's internal architecture, such as parameters controlling how the peripheral vision and memory modules work. Critically, since these parameters are interrelated in complex ways, optimizing them can be viewed as a black box optimization problem, which is especially challenging for non-experts. Although existing optimization-as-a-service platforms (e.g., Vizier, Optuna) can handle such problems, they are impractical for RL systems, as users must manually map each parameter to different components, making the process cumbersome and error-prone. They also require deep understanding of the optimization process, limiting their application outside ML experts and restricting access for fields like cognitive science, which models human decision-making. To tackle these challenges, we present AgentForge, a flexible low-code framework to optimize any parameter set across an RL system. AgentForge allows the user to perform individual or joint optimization of parameter sets. An optimization problem can be defined in a few lines of code and handed to any of the interfaced optimizers. We evaluated its performance in a challenging vision-based RL problem. AgentForge enables practitioners to develop RL agents without requiring extensive coding or deep expertise in optimization.