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

2024-10-25 · Francisco Erivaldo Fernandes Junior, Antti Oulasvirta

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.

📄 PDF Abstract BibTeX arXiv:2410.19528

Code (1)

feferna/AgentForge 공식 구현 pytorch

Tasks

Decision Making Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling

2025-11-08 · Qi Wang, Hongzhi Zhang, Jia Fu, Kai Fu 외 arxiv

Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In this study, we present a comprehensive and …

Reinforcement Learning

AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering

2026-04-13 · Rajesh Kumar, Waqar Ali, Junaid Ahmed, Najma Imtiaz Ali 외 arxiv

Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification optional. We introduce execution-grounded verification as a first-class p…

A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge

2026-01-19 · Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari arxiv

The emergence of LLMs has catalyzed a paradigm shift in autonomous agent development, enabling systems capable of reasoning, planning, and executing complex multi-step tasks. However, existing agent frameworks often suff…

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

2017-12-02 · Lianmin Zheng, Jiacheng Yang, Han Cai, Wei-Nan Zhang 외

We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting t…

GPU Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

2024-10-23 · Rohit Bokade, Xiaoning Jin

Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face ch…

Management Multi-agent Reinforcement Learning Traffic Signal Control