Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba
Why It Matters
What makes this one worth your time
This approach could lead to more efficient and capable autonomous systems by leveraging the strengths of both LLMs and RL in complex environments.
Integrating LLMs with RL enhances performance in complex decision-making tasks.
Summary
The paper proposes a hybrid agent architecture that combines Large Language Models (LLMs) for high-level planning with Reinforcement Learning (RL) for low-level action optimization to improve performance on complex sequential decision tasks.
Key contributions
- Introduction of a hybrid LLM-RL architecture for sequential decision tasks.
- Demonstration of improved performance over RL-only and LLM-only baselines.
Notable insights
- Combining LLM-driven planning with RL action optimization can improve sample efficiency and success rates.
- Using LLMs for generating subgoals and structured plans provides contextual guidance for RL agents.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2608.03502v2 Announce Type: replace Abstract: 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 contextual guidance, while the RL agent refines low-level actions through interaction with the environment. Experiments on sequential decision tasks demonstrate improved sample efficiency, higher success rates, and more coherent action trajectories compared to RL-only and LLM-only baselines. This hybrid paradigm highlights a promising direction for building more capable autonomous systems.