Yawei Wang

Jiongxiao Wang, Qiaojing Yan, Yawei Wang et al.

Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely primarily on LLM prompting, making consistent skill library implementation challenging. To overcome these challenges, we propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library. Specifically, we introduce Skill Augmented GRPO for self-Evolution (SAGE), a novel RL framework that systematically incorporates skills into learning. The framework's key component, Sequential Rollout, iteratively deploys agents across a chain of similar tasks for each rollout. As agents navigate through the task chain, skills generated from previous tasks accumulate in the library and become available for subsequent tasks. Additionally, the framework enhances skill generation and utilization through a Skill-integrated Reward that complements the original outcome-based rewards. Experimental results on AppWorld demonstrate that SAGE, when applied to supervised-finetuned model with expert experience, achieves 8.9% higher Scenario Goal Completion while requiring 26% fewer interaction steps and generating 59% fewer tokens, substantially outperforming existing approaches in both accuracy and efficiency.

Yawei Wang, Xiu Li

For adversarial imitation learning algorithms (AILs), no true rewards are obtained from the environment for learning the strategy. However, the pseudo rewards based on the output of the discriminator are still required. Given the implicit reward bias problem in AILs, we design several representative reward function shapes and compare their performances by large-scale experiments. To ensure our results' reliability, we conduct the experiments on a series of Mujoco and Box2D continuous control tasks based on four different AILs. Besides, we also compare the performance of various reward function shapes using varying numbers of expert trajectories. The empirical results reveal that the positive logarithmic reward function works well in typical continuous control tasks. In contrast, the so-called unbiased reward function is limited to specific kinds of tasks. Furthermore, several designed reward functions perform excellently in these environments as well.