AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design
On the map: Deep reinforcement learning methods → RL training of language and agent models
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL training of language and agent models, 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.
Builds on
13 of 40 references.
Closest ancestors · 1
Same direction, newest first
- 2024EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning15 Apr
Other references · 12
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
- 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 162
- 2020Hyperparameter Selection for Offline Reinforcement Learningcited by 114
- 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 70
- 2022Intelligent problem-solving as integrated hierarchical reinforcement learningcited by 21
- 2022Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimizationcited by 18
- 2022Automated Reinforcement Learning: An Overviewcited by 9
- 2023Tune As You Scale: Hyperparameter Optimization For Compute Efficient Trainingcited by 9
- 2021Compositional Learning-based Planning for Vision POMDPscited by 8
- 2018Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classificationcited by 7
- 2021Efficient Hyperparameter Optimization for Physics-based Character Animationcited by 5
- 2022Cognitive Models as Simulators: The Case of Moral Decision-Makingcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Simulation-based Optimization for Augmented Readingcited by 0
- 2026Simulation-based Optimization for Augmented Reading26 Feb 2026
Similar, not cited
Close in wording, no citation either way
- 2025AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
- 2025Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
- 2025Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
- 2025AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
- 2024RL-GPT: Integrating Reinforcement Learning and Code-as-policy
- 2026SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
- 2025AgentFly: Extensible and Scalable Reinforcement Learning for LM Agents
- 2024A Research Agenda for Usability and Generalisation in Reinforcement Learning