Study Maps Reinforcement Learning's Shift From Simulations to Language-Driven AI Agents

Researchers analyzed 2,000+ academic papers to document how reinforcement learning environments evolved from isolated physics simulations to generalist agents powered by large language models. The study identifies a fundamental split in the field between LLM-dominated systems and domain-specific approaches, offering insights for designing next-generation AI that bridges physical control with reasoning.