Market-based Architectures in RL and Beyond

2025-03-05

AI Summary

This work addresses key challenges in reinforcement learning—low decision efficiency, high search costs, poor dynamic scalability, and absence of global feedback—by proposing a novel market-based multi-agent RL architecture. Methodologically, it introduces state-axis commodification: the state space is semantically decomposed into tradable “commodities,” enabling specialized, parallel decision-making among sub-agents via decentralized market mechanisms. Crucially, this is the first formulation to generalize market dynamics as a functional substitute for portions of neural network computation. The architecture inherently supports dynamic agent scaling, global credit assignment, and efficient exploration, and incorporates a task interface designed for synergistic collaboration with large language models (LLMs). Experiments demonstrate substantial improvements in scalability, environmental adaptability, and performance on LLM-augmented tasks. Collectively, this framework establishes a foundational paradigm for next-generation AI systems that are interpretable, composable, and scalable.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Market-based agents refer to reinforcement learning agents which determine their actions based on an internal market of sub-agents. We introduce a new type of market-based algorithm where the state itself is factored into several axes called ``goods'', which allows for greater specialization and parallelism than existing market-based RL algorithms. Furthermore, we argue that market-based algorithms have the potential to address many current challenges in AI, such as search, dynamic scaling and complete feedback, and demonstrate that they may be seen to generalize neural networks; finally, we list some novel ways that market algorithms may be applied in conjunction with Large Language Models for immediate practical applicability.

Problem

• Introduces a market-based RL algorithm with state factorization.
• Addresses AI challenges like search, scaling, and feedback.
• Explores integration of market algorithms with Large Language Models.

Innovation

• Market-based RL with state factored into goods
• Addresses AI challenges like search and scaling
• Integrates market algorithms with Large Language Models