The potential of LLM-based agents for embodied actions. Before the widespread rise of LLMs,
researchers tended to use methods like reinforcement learning to explore the embodied actions of
agents. Despite the extensive success of RL-based embodiment [359; 360; 361], it does have certain
limitations in some aspects. In brief, RL algorithms face limitations in terms of data efficiency,
generalization, and complex problem reasoning due to challenges in modeling the dynamic and
often ambiguous real environment, or their heavy reliance on precise reward signal representations
[362]. Recent studies have indicated that leveraging the rich internal knowledge acquired during the
pre-training of LLMs can effectively alleviate these issues [120; 187; 258; 363].
TheRiseandPotentialofLargeLanguageModel BasedAgents