Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimization. The proposed architecture leverages the LLM to generate subgoals, structured plans, and co...
| Authors | Christophe D. Hounwanou, John Emeka Eze, Yaé Ulrich Gaba |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 18 August 2026 |
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