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New AI system GARDEN automates reward design for multi-agent learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GARDEN, a novel system for autonomous reward design in cooperative multi-agent reinforcement learning. GARDEN aims to create a single reward program that can be effectively reused across a family of related scenarios, reducing the need for manual reward engineering. The system investigates environment code, training records, and agent behavior, using an LLM reward engineer to develop and test rewards. In tests on the Overcooked and SMAX benchmarks, GARDEN significantly improved task returns and win rates compared to default rewards and other methods. AI

IMPACT Automates reward function design for multi-agent systems, potentially accelerating development and improving performance in complex cooperative tasks.

RANK_REASON This is a research paper detailing a new method for AI reward design. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI system GARDEN automates reward design for multi-agent learning

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This is a research paper detailing a new method for AI reward design. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dogan Urgun ·

    GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

    arXiv:2610.37926v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because …

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