arXiv:2605.31444v1 Announce Type: new Abstract: Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, making learning and generalisation challenging. Abstraction and approximation are therefore essential. Relational Reinforcement Learning (RRL) offers a way to reason about objects and their relations, and the CARCASS framework by Martijn van Otterlo demonstrates how logical representations can model Markov Decision Processes (MDPs) in first-order domains. Originally implemented in Prolog, CARCASS lev
The increasing complexity of real-world AI problems, particularly in Reinforcement Learning, is driving the need for more sophisticated abstraction methods to manage huge state spaces and improve learning efficiency.
This research addresses a fundamental limitation in reinforcement learning by proposing a method that could significantly enhance the scalability and generalisation capabilities of autonomous agents in complex environments.
The development of more powerful abstraction techniques, like those leveraging Answer Set Programming (ASP), could make RL models more robust and applicable to a wider range of real-world, high-dimensional challenges.
- · AI researchers
- · Robotics
- · Logistics and autonomous systems
- · AI development platforms
- · Developers relying solely on brute-force RL methods
- · Systems with high computational demands for RL
More efficient and generalizable reinforcement learning models are developed.
Autonomous agents, particularly AI agents and humanoid robots, become more capable of reasoning and planning in complex, dynamic environments.
This could accelerate the deployment of autonomous systems in critical industries, potentially impacting workforce structures and operational efficiencies across sectors.
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Read at arXiv cs.AI