arXiv:2506.09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent. We propose learning the minimum action distance (MAD), defined as the minimum number of actions required to transition between states, as a fundamental metric that captures the underlying structure of an environment. MAD naturally enables critical downstream tasks such as goal-conditioned reinforcement learning and reward shap
The continuous advancements in reinforcement learning and the push for more autonomous and efficient AI systems necessitate novel approaches to state representation and goal conditioning without relying on dense reward signals.
This development proposes a foundational shift in how AI agents learn about and navigate environments, potentially enabling more robust and generalizable AI by reducing reliance on explicit rewards or action logs.
AI agents might no longer require meticulously designed reward functions or recorded actions to understand environmental dynamics, accelerating development in complex, partially observable, or sparse-reward environments.
- · AI agents developers
- · Robotics
- · Reinforcement learning researchers
- · Autonomous systems
- · Expert-driven reward engineering
- · Brute-force simulation approaches
AI models will learn environmental structures more efficiently and with less human supervision.
This efficiency could lead to faster deployment of AI agents in real-world scenarios with less data.
Generalized AI agents capable of learning and adapting across diverse tasks and environments with minimal prior knowledge could emerge.
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Read at arXiv cs.AI