  Large language models (LLMs) have demonstrated remarkable advancements and
have attracted significant efforts to develop LLMs into agents capable of
executing intricate multi-step decision-making tasks beyond traditional NLP
applications. Existing approaches to LLM-based decision-making predominantly
build upon the manually-designed external performance metrics to guide the
decision-making process. However, reliance on the external performance metrics
as prior is problematic in real-world scenarios, where such prior may be
unavailable, flawed, or even erroneous. For genuine autonomous decision making,
it is imperative for the agent to develop its rationality from its posterior
experiences to judge decisions independently. Central to the development of
rationality is the construction of an internalized utility judgment, capable of
assigning numerical utilities to each decision. This paper proposes RadAgent
(Rational Decision-Making Agent), which fosters the development of its
rationality through an iterative framework involving Experience Exploration and
Utility Learning. Within this framework, Elo-based Utility Construction is
devised to assign Elo scores to individual decision steps to judge their
utilities via pairwise comparisons. Consequently, these Elo scores guide the
decision-making process to derive optimal outcomes. Experimental results on the
ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving
over 10% improvement in Pass Rate on diverse tasks. It offers higher-quality
solutions and reduces costs (ChatGPT API calls), highlighting its effectiveness
and efficiency.
