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VEG: Verbal ϵ -greedy for Semantic Exploration in Multi-Turn RL Agents

Yongchang Hao Jie Hao Yongsheng Mei Ze Ye Junyi Chai Bin Guo Benjamin Z. Yao Chenlei Guo Lili Mou Amazon
· 0 citations · 32 references

TL;DR

This work proposes VEG (verbal ϵ -greedy), a novel framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space and achieves superior accuracy compared to standard RL baselines.

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