RLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms

Key points

• Joint search over coupled algorithmic components is difficult to scale: simultaneous changes can disrupt learning, while isolated changes overlook their dependencies.1
• We introduce RLDiscover, a framework for the self-evolution of model-free deep RL algorithms.1
• Progressive Co-Evolution advances from targeted component edits to joint evolution, while Progressive Probabilistic Evaluation balances search breadth and evaluation fidelity through staged training and repeated evaluation.1
• These findings point toward a broader role for self-evolution in AI: discovering interpretable algorithms that improve how agents learn.1

Sources (1)

• [1]RLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 11:26 PM

  LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self-evolve to improve how agents learn.

  Joint search over coupled algorithmic components is difficult to scale: simultaneous changes can disrupt learning, while isolated changes overlook their dependencies.

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