Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning

2025-07-21

AI Summary

To address the challenges of sparse or absent rewards in reinforcement learning and unlabeled, incomplete expert demonstrations, this paper proposes the Mixture of Autoencoder Experts (MAE) model. MAE jointly models diverse behavioral patterns and missing information, mapping implicit state-wise similarity between agent trajectories and expert demonstrations onto intrinsic reward signals—enabling directed exploration without explicit labels or complete expert trajectories. By integrating autoencoder architectures with behavior similarity metrics, MAE efficiently encodes high-dimensional state-action spaces and generates shaped rewards. Experimental results demonstrate that MAE exhibits robust performance across both sparse- and dense-reward environments, significantly improving exploration efficiency and policy performance when expert demonstrations are scarce or of low quality.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Recent trends in Reinforcement Learning (RL) highlight the need for agents to learn from reward-free interactions and alternative supervision signals, such as unlabeled or incomplete demonstrations, rather than relying solely on explicit reward maximization. Additionally, developing generalist agents that can adapt efficiently in real-world environments often requires leveraging these reward-free signals to guide learning and behavior. However, while intrinsic motivation techniques provide a means for agents to seek out novel or uncertain states in the absence of explicit rewards, they are often challenged by dense reward environments or the complexity of high-dimensional state and action spaces. Furthermore, most existing approaches rely directly on the unprocessed intrinsic reward signals, which can make it difficult to shape or control the agent's exploration effectively. We propose a framework that can effectively utilize expert demonstrations, even when they are incomplete and imperfect. By applying a mapping function to transform the similarity between an agent's state and expert data into a shaped intrinsic reward, our method allows for flexible and targeted exploration of expert-like behaviors. We employ a Mixture of Autoencoder Experts to capture a diverse range of behaviors and accommodate missing information in demonstrations. Experiments show our approach enables robust exploration and strong performance in both sparse and dense reward environments, even when demonstrations are sparse or incomplete. This provides a practical framework for RL in realistic settings where optimal data is unavailable and precise reward control is needed.

Problem

• Utilizing unlabeled incomplete data for RL exploration
• Overcoming dense reward high-dimensional space challenges
• Shaping intrinsic rewards for targeted expert-like behaviors

Innovation

• Mixture of Autoencoder Experts for diverse behaviors
• Mapping function transforms similarity to intrinsic reward
• Flexible exploration with incomplete expert demonstrations