Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
arXiv:2602.17931v1 Announce Type: new Abstract: In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs) for subgoal discovery and trajectory guidance. While LLMs can support exploration, frequent reliance on LLM calls raises concerns about scalability and reliability. We address these challenges by constructing a memory graph that encodes subgoals and trajectories from both LLM guidance and the agent's own successful rollouts. From this graph, we derive a utility function that evaluates how closely the agent's trajectories align with prior successful strategies. This utility shapes the advantage function, providing the critic with additional guidance without altering the reward. Our method relies primarily on offline input and only occasional online queries, avoiding dependence on continuous LLM supervision
arXiv:2602.17931v1 Announce Type: new Abstract: In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs) for subgoal discovery and trajectory guidance. While LLMs can support exploration, frequent reliance on LLM calls raises concerns about scalability and reliability. We address these challenges by constructing a memory graph that encodes subgoals and trajectories from both LLM guidance and the agent's own successful rollouts. From this graph, we derive a utility function that evaluates how closely the agent's trajectories align with prior successful strategies. This utility shapes the advantage function, providing the critic with additional guidance without altering the reward. Our method relies primarily on offline input and only occasional online queries, avoiding dependence on continuous LLM supervision. Preliminary experiments in benchmark environments show improved sample efficiency and faster early learning compared to baseline RL methods, with final returns comparable to methods that require frequent LLM interaction.
Executive Summary
This article proposes a novel approach to reinforcement learning (RL) by leveraging large language models (LLMs) for subgoal discovery and trajectory guidance. The authors introduce a memory-based advantage shaping method that constructs a graph encoding subgoals and trajectories from both LLM guidance and the agent's own successful rollouts. This approach enables the agent to learn more efficiently, reducing reliance on continuous LLM supervision and improving sample efficiency. Preliminary experiments demonstrate promising results, with improved early learning and comparable final returns to methods requiring frequent LLM interaction.
Key Points
- ▸ Memory-based advantage shaping for LLM-guided RL
- ▸ Reducing reliance on continuous LLM supervision
- ▸ Improving sample efficiency in sparse or delayed reward environments
Merits
Efficient Learning
The proposed method enables the agent to learn more efficiently, reducing the number of interactions required for learning.
Scalability
The approach avoids dependence on continuous LLM supervision, making it more scalable and reliable.
Demerits
Complexity
The construction of a memory graph and derivation of a utility function may add complexity to the RL framework.
Expert Commentary
The proposed memory-based advantage shaping method offers a promising approach to addressing the challenges of RL in sparse or delayed reward environments. By leveraging LLMs for subgoal discovery and trajectory guidance, the authors demonstrate improved sample efficiency and faster early learning. The approach's ability to reduce reliance on continuous LLM supervision is particularly noteworthy, as it enhances scalability and reliability. However, further research is needed to fully explore the potential of this method and its applications in various domains.
Recommendations
- ✓ Further experimentation in diverse environments to validate the approach's effectiveness
- ✓ Investigation of the method's potential applications in areas like autonomous systems and decision-making under uncertainty
