Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL (2409.12798v1)

Abstract: The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory for their ability to achieve a goal. However, when feedback is delayed and sparse, the learning signal is poor, and action evaluation becomes harder. Canonical solutions, such as reward shaping and options, require extensive domain knowledge and manual intervention, limiting their scalability and applicability. In this work, we lay the foundations for Credit Assignment with LLMs (CALM), a novel approach that leverages LLMs to automate credit assignment via reward shaping and options discovery. CALM uses LLMs to decompose a task into elementary subgoals and assess the achievement of these subgoals in state-action transitions. Every time an option terminates, a subgoal is achieved, and CALM provides an auxiliary reward. This additional reward signal can enhance the learning process when the task reward is sparse and delayed without the need for human-designed rewards. We provide a preliminary evaluation of CALM using a dataset of human-annotated demonstrations from MiniHack, suggesting that LLMs can be effective in assigning credit in zero-shot settings, without examples or LLM fine-tuning. Our preliminary results indicate that the knowledge of LLMs is a promising prior for credit assignment in RL, facilitating the transfer of human knowledge into value functions.

Analyzing Credit Assignment with LLMs in Reinforcement Learning: A Critical Overview

The paper "CALM: Credit Assignment with LLMs" by Pignatelli et al. addresses a fundamental challenge in reinforcement learning (RL): the credit assignment problem (CAP). CAP concerns identifying the contribution of individual actions to the final outcome, especially when rewards are sparse and delayed. Traditional solutions like reward shaping and hierarchical reinforcement learning (HRL) require extensive domain knowledge, limiting their scalability and generalizability. This paper explores leveraging LLMs for credit assignment, ushering in a novel approach named CALM.

Key Contributions

1. Integration of LLMs in RL for Credit Assignment:
   • CALM uses LLMs to decompose RL tasks into subgoals and assess the completion of these subgoals within state-action transitions.
   • By assigning auxiliary rewards each time an option (or subgoal) is completed, CALM provides a supplemental reward signal that aids learning in scenarios with sparse and delayed rewards.
2. Zero-Shot Capability of LLMs:
   • The paper evaluates LLMs in zero-shot settings, revealing their potential in automating reward shaping without fine-tuning or task-specific examples.
   • Using a dataset from MiniHack, the results suggest that LLMs can effectively perform credit assignment, aligning LLM knowledge with human annotations.
3. Experiment Design and Comparative Analysis:
   • The authors employ diverse LLMs, including Meta-Llama-3 and Mixtral models, comparing their performance in tasks involving state-action transitions.
   • They analyze the impact of different observational settings (cropped vs. full game screens) and the provision of subgoals (predetermined vs. discovered by LLMs).

Experimental Findings

Subgoal Verification

In experiments where models were provided with a set of subgoals, LLMs showed proficiency in recognizing when an option had terminated. For instance, Mixtral-8x7B-Instruct achieved a high F1 score (0.74 with game screen observations), indicating strong alignment with human annotations when evaluating action effectiveness in transitions.

Subgoal Discovery

When LLMs were tasked with discovering subgoals, the results were promising but showed variability. Models like Meta-Llama-3-70B-Instruct exhibited impressive performance (F1 score of 0.82 with game screen observations), demonstrating that LLMs can autonomously identify valuable subgoals and use them to guide reward shaping.

Observation Variance

Cropped observations tended to enhance performance across the board. This suggests that reducing the information load by focusing the observation field helps models concentrate on pertinent details, thereby improving credit assignment accuracy.

Implications

1. Theoretical Implications:
   • The integration of LLMs into RL for credit assignment introduces a paradigm where domain knowledge embedded in pre-trained models can be exploited to automate complex tasks, thereby reducing human dependency.
   • The proposition that LLMs can discern and utilize causal patterns in action-sequence data aligns with recent advances in causal reasoning in AI.
2. Practical Implications:
   • CALM can facilitate the transfer of human knowledge into RL agents more effectively and efficiently. This ability to function without extensive human-engineered rewards in diverse RL environments points to practical scalability.
   • The successful application of CALM to MiniHack suggests broader applicability, including more complex RL scenarios like autonomous driving or strategic game playing.
3. Future Developments:
   • Future research should expand CALM to online RL settings to validate its effectiveness in dynamic, real-time environments.
   • Exploring the fusion of multimodal LLMs (Vision-LLMs) could generalize CALM beyond text-based observations, bridging the gap between virtual simulations and real-world tasks.

Conclusion

CALM represents a significant advancement in leveraging LLMs for automating credit assignment in RL. By systematically decomposing tasks into manageable subgoals and harnessing the extensive knowledge baked into LLMs, this approach seeks to automate reward shaping efficiently and scale across various domains with minimal human intervention. While promising, future research should validate these initial findings in more complex, online settings and extend the approach to multimodal environments, fully realizing the potential of LLMs in revolutionizing RL.