Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning (2412.11120v2)

Abstract: Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attributions stemming from overlooking the multifaceted nature of mission performance evaluation. Hopefully, LLM encompasses fruitful decision-making knowledge and provides a plausible tool for reward redistribution. Even so, deploying LLM in this case is non-trivial due to the misalignment between linguistic knowledge and the symbolic form requirement, together with inherent randomness and hallucinations in inference. To tackle these issues, we introduce LaRe, a novel LLM-empowered symbolic-based decision-making framework, to improve credit assignment. Key to LaRe is the concept of the Latent Reward, which works as a multi-dimensional performance evaluation, enabling more interpretable goal attainment from various perspectives and facilitating more effective reward redistribution. We examine that semantically generated code from LLM can bridge linguistic knowledge and symbolic latent rewards, as it is executable for symbolic objects. Meanwhile, we design latent reward self-verification to increase the stability and reliability of LLM inference. Theoretically, reward-irrelevant redundancy elimination in the latent reward benefits RL performance from more accurate reward estimation. Extensive experimental results witness that LaRe (i) achieves superior temporal credit assignment to SOTA methods, (ii) excels in allocating contributions among multiple agents, and (iii) outperforms policies trained with ground truth rewards for certain tasks.

• The paper presents the LaRe framework, leveraging LLMs to improve credit assignment in episodic reinforcement learning.
• It introduces latent rewards through environment prompting and self-verification, ensuring stable and interpretable reward signals.
• Empirical results demonstrate that LaRe outperforms traditional methods in complex multi-agent scenarios and tasks with large state spaces.

Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning

The paper presents a novel framework termed LaRe, designed to address the challenges associated with credit assignment in episodic reinforcement learning (RL) environments. This research acknowledges the complexities arising from delayed and sparse feedback in RL, proposing a method that leverages LLMs to facilitate more precise credit assignment via the innovative concept of Latent Reward.

Background and Challenges

Episodic RL often confronts the difficulty of distributing credit for actions in scenarios where feedback is delayed and sparse, a typical issue in many real-world applications. Traditional methods have attempted to redistribute episodic rewards back to individual decisions but have struggled due to redundant information and ambiguous credit mapping, which can hinder training and lead to ineffectual policy development.

Proposal of LaRe Framework

The LaRe framework is grounded in the introduction of Latent Rewards, which encapsulate task performance across multiple dimensions, improving upon previous approaches by providing semantically interpretable intermediaries for reward function construction. The framework is built on two key components:

1. Environment Prompting: A standardized prompt design enables the LLM to extract relevant task information, thereby instructing it to adaptively encode environment information into latent rewards.
2. Latent Reward Self-verification: LaRe ensures reliability and stability in LLM inference through a process that includes generating multiple candidate responses, synthesizing them into a singular improved model, and validating the execution of these models against task-relevant parameters.

Theoretical and Empirical Analysis

The theoretical underpinning is provided by the construction of a probabilistic model of episodic rewards that integrates latent rewards, positing a framework for redundancy reduction and improved reward modeling precision. The authors statistically validate that this model outperforms traditional state-based methods due to its enhanced alignment with reward-relevant features.

In empirical evaluations, LaRe demonstrates superior performance across various environments, including complex multi-agent scenarios found in the MuJoCo and Multi-Agent Particle Environment (MPE). The method consistently outperforms leading algorithms in these contexts. Notably, in tasks with large state spaces and multiple agents, LaRe's multifaceted latent rewards enable more accurate and interpretable policy improvements, highlighting the utility of integrating LLMs for complex task evaluations.

Implications and Future Directions

The implications of this research are significant in the field of AI, particularly for applications requiring sophisticated temporal and agent-level credit assignment mechanisms. By leveraging LLMs, the research introduces an innovative methodology that can seamlessly integrate task prior knowledge, providing a scalable solution to a long-standing challenge in RL.

Looking forward, the paper suggests potential extensions of LaRe to image-based tasks with complex visual inputs, potentially broadening the applicability of the framework through the use of multimodal LLMs. Additionally, the intersection of offline RL environments presents a fertile ground for future research, promising further advancements in task generalization and performance optimization in RL.

In conclusion, LaRe stands out as a significant contribution to reinforcement learning methodology, introducing a robust, LLM-based paradigm for credit assignment that offers tangible benefits in both interpretability and computational efficiency. The framework effectively addresses key shortcomings of existing methods while laying the groundwork for future innovations in AI-driven decision-making processes.