\section{Reinforcement Learning for LLM-based Agents}
Using RL to train agents is 一个标准的做法在传统的机器学习中。 in this context, the agent has always been the central decision-making entity interacting with an environment. However, the advent of LLMs has fundamentally transformed the motivation, application scenarios, and action spaces of RL-driven agents.
% Using RL to train agents is 一个标准的做法在传统的机器学习中。 in this context, the agent has always been the central decision-making entity interacting with an environment. However, the advent of LLMs has fundamentally transformed the motivation, application scenarios, and action spaces of RL-driven agents.
In classical RL-driven agents, an agent is typically a domain-specific decision model trained from scratch. For example, in tasks such as mastering Atari games, playing Go, or robotic locomotion, researchers construct a relatively small-scale policy network situated within a highly structured simulated environment with predefined state and action spaces. Through millions of trial-and-error interactions, the agent gradually learns to maximize cumulative rewards. In these classic settings, the primary objective of RL is to explore and synthesize an optimal execution policy from a blank slate.
