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

arXiv: https://arxiv.org/abs/2410.19528 · HTML: https://mlsanity.com/paper/2410.19528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Builds on (most cited first, 13 of 13)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [Hyperparameter Selection for Offline Reinforcement Learning](https://mlsanity.com/paper/2007.09055/) (2020)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Intelligent problem-solving as integrated hierarchical reinforcement learning](https://mlsanity.com/paper/2208.08731/) (2022)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [Automated Reinforcement Learning: An Overview](https://mlsanity.com/paper/2201.05000/) (2022)
- [Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training](https://mlsanity.com/paper/2306.08055/) (2023)
- [Compositional Learning-based Planning for Vision POMDPs](https://mlsanity.com/paper/2112.09456/) (2021)
- [EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning](https://mlsanity.com/paper/2404.10163/) (2024)
- [Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification](https://mlsanity.com/paper/1803.06492/) (2018)
- [Efficient Hyperparameter Optimization for Physics-based Character Animation](https://mlsanity.com/paper/2104.12365/) (2021)
- [Cognitive Models as Simulators: The Case of Moral Decision-Making](https://mlsanity.com/paper/2210.04121/) (2022)

## Built on by (most cited first, 1 of 1)

- [Simulation-based Optimization for Augmented Reading](https://mlsanity.com/paper/2602.22735/) (2026)

## Abstract (arXiv)

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.