RF-Agent: Automated Reward Function Design via Language Agent Tree Search

TL;DR

RF-Agent is a framework that treats LLMs as language agents and frames reward function design as a sequential decision-making process, enhancing optimization through better contextual reasoning and integrates Monte Carlo Tree Search to manage the reward design and optimization process.

Abstract

Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward functions. These methods typically rely on training results as feedback, iteratively generating new reward functions with greedy or evolutionary algorithms. However, they suffer from poor utilization of historical feedback and inefficient search, resulting in limited improvements in complex control tasks. To address this challenge, we propose RF-Agent, a framework that treats LLMs as language agents and frames reward function design as a sequential decision-making process, enhancing optimization through better contextual reasoning. RF-Agent integrates Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi-stage contextual reasoning ability of LLMs. This approach better utilizes historical information and improves search efficiency to identify promising reward functions. Outstanding experimental results in 17 diverse low-level control tasks demonstrate the effectiveness of our method. The source code is available at https://github.com/deng-ai-lab/RF-Agent.

Figures (11)

• Figure 1: Quick view. (a) Reshaping LLM-based Reward Function Design Problem from sequential decision-making view. (b) Abstract pipeline comparison between different approaches. (c) Comparison between different methods on max success rates with the numbers of complete training times.
• Figure 2: Illustration of our RF-Agent with (a) an exemplary reward function optimization path from the tree, (b) total tree growth process, and (c) iteration based on MCTS. Please refer to the Appendix \ref{['suppB']} for prompts used in RF-Agent, mainly including initialization, expansion, and other process.
• Figure 3: Success rates comparison with standard deviation upper bar on BiDex Expert-Easy/Hard.
• Figure 4: Success rates with exploration steps under reward functions by different methods.
• Figure 5: Reward function optimization performance with sampling counts.