Guiding Pretraining in Reinforcement Learning with Large Language Models (2302.06692v2)

Abstract: Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by rewarding agents for visiting novel states or transitions, but these methods offer limited benefits in large environments where most discovered novelty is irrelevant for downstream tasks. We describe a method that uses background knowledge from text corpora to shape exploration. This method, called ELLM (Exploring with LLMs) rewards an agent for achieving goals suggested by a LLM prompted with a description of the agent's current state. By leveraging large-scale LLM pretraining, ELLM guides agents toward human-meaningful and plausibly useful behaviors without requiring a human in the loop. We evaluate ELLM in the Crafter game environment and the Housekeep robotic simulator, showing that ELLM-trained agents have better coverage of common-sense behaviors during pretraining and usually match or improve performance on a range of downstream tasks. Code available at https://github.com/yuqingd/eLLM.

• The paper introduces ELLM, leveraging LLMs to provide intrinsic rewards in RL by generating human-meaningful, contextually relevant goals.
• The method outperforms standard techniques like RND and APT, achieving superior exploration coverage in simulated environments such as Crafter and Housekeep.
• The research highlights the potential of LLMs to drive goal-directed exploration in RL while addressing challenges in task relevancy and feasibility.

Insights into Guiding Pretraining in Reinforcement Learning with LLMs

The paper "Guiding Pretraining in Reinforcement Learning with LLMs" explores the novel utilization of LLMs to enhance exploration strategies in reinforcement learning (RL) by providing agents with human-meaningful and context-sensitive goals. Specifically, the authors introduce the method Exploring with LLMs (ELLM), which employs LLMs for intrinsic motivation by shaping exploratory objectives based on background knowledge extracted from large text corpora. ELLM exemplifies a paradigm that integrates LLMs with RL to direct the agent's learning trajectory toward potentially beneficial behaviors even in the absence of explicit external rewards.

Theoretical and Practical Contributions

A significant contribution of this work lies in addressing the challenge of designing effective intrinsic reward systems for RL agents operating in environments devoid of dense reward functions. The paper suggests leveraging LLMs to suggest goals that are inherently human-meaningful and context-relevant, thereby bypassing the inefficiencies of prevailing techniques that often struggle to focus exploration on practical objectives amidst a broad novelty-driven search.

Key theoretical advancements include:

• The formalization of ELLM, which combines the potential of LLMs in context understanding with RL's need for rich exploratory signals.
• The alignment of RL exploration with intrinsic motivation via goal diversity, common-sense validation, and context sensitivity, which are critical properties for effective reinforcement learning in complex environment settings.

Practically, the paper demonstrates that ELLM outperforms standard methods such as Random Network Distillation (RND) and Active Pretraining (APT) by achieving superior exploration coverage in simulated environments like Crafter and Housekeep. Notably, the ELLM outperforms existing methods by aligning exploration with both intrinsic rewards and anticipated downstream utility. The integration of context-specific prompts with LLMs allows for precise and relevant goal suggestion, ensuring more directed and beneficial exploration.

Results and Implications

The strong empirical results highlight ELLM's capabilities in achieving better coverage of common-sense behaviors, elucidating the capability of LLMs to shape agent exploration effectively. ELLM was shown to match or surpass baseline performances in challenging environments, successfully translating LLM-suggested goals into achieved behaviors that align with theoretically valuable outcomes.

Contrarily, it is also noted that there are instances when LLMs suggest infeasible or nonsensical tasks due to limitations in task-relevant information, aligning with challenges in LLMs concerning specificity and task bias. Nevertheless, the approach’s reliance on known text corpora for likelihood estimation and its usefulness in procedurally-generated or partially observable environments showcase its robustness.

For future developments, ELLM sets the groundwork for broader integration of LLMs in RL, suggesting opportunities to refine LLM prompt engineering or combining with other intrinsic motivation models to enhance goal relevancy further. The implications of using rich language spaces to inform exploration in RL present exciting avenues for developing systems that learn aligned with human intuition and practicality.

In conclusion, this paper marks a significant step toward enriching reinforcement learning frameworks with pretrained LLMs to enhance exploratory efficiency without explicit external reward definition. Through ELLM, the potential for language-assisted, context-driven exploration emerges as a promising research trajectory, which not only extends the horizons of RL applications but also fosters the development of human-aligned autonomous systems.