arXiv:2606.26327v1 Announce Type: new Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each candidate must be trained before evaluation, and the design space is open-ended. To address these challenges, we introduce EVOM, an agentic meta-evolution framework for discovering high-performance actor-critic architectures. We frame architecture search as a bi-level optimization: an inner loop trains weights via the low-fidelity proximal policy optimization (PPO), while an outer loop drives meta-evolu
The paper leverages recent advancements in meta-learning and agentic systems, aligning with the current push towards more autonomous AI development and optimization.
Automating the design of high-performance actor-critic architectures accelerates reinforcement learning research and application, reducing reliance on manual expert tuning.
The development of RL agents becomes less dependent on human intuition for architecture design, potentially leading to faster discovery of more efficient and powerful AI systems.
- · AI research labs
- · Robotics companies
- · Autonomous systems developers
- · Reinforcement learning applications
- · AI researchers specializing in manual architecture design
- · Companies without access to advanced meta-evolutionary frameworks
Reduced architectural bottleneck in advanced reinforcement learning agent development.
Faster development and deployment of complex AI agents across various domains, including robotics and strategic decision-making.
Enhanced AI capabilities leading to new breakthroughs in fields previously limited by AI design complexity, accelerating the pace of general AI advancement.
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Read at arXiv cs.LG