Agent Self Evolution

Agent self-evolution explores methods for creating artificial agents that can autonomously improve their capabilities over time, mirroring biological evolution. Current research focuses on using large language models (LLMs) within simulated environments to evolve agents through reinforcement learning, adversarial training, and symbolic learning techniques, often employing population-based approaches and GPU acceleration for efficiency. This research aims to create more robust, adaptable, and generalizable AI agents, with applications ranging from legal and medical simulations to robotics and potentially contributing to a deeper understanding of intelligence itself.