Answer-Set-Programming-based Abstractions for Reinf... | AI Research

Reinforcement Learning (RL) allows autonomous agents to learn behaviors through trial and error. However, when faced with complex environments, the number of possible states and actions can become so large that traditional learning methods struggle to function efficiently. This paper explores a way to simplify these massive environments by using logical abstractions, allowing agents to reason about objects and their relationships rather than processing every individual state separately.

Bridging Logic and Learning

The authors focus on the CARCASS framework, a method for Relational Reinforcement Learning (RRL) that uses logic to group similar states together. By defining abstract rules, an agent can treat different but logically equivalent situations as the same "abstract state." While this framework was originally implemented in Prolog, the authors propose using Answer-Set Programming (ASP) instead. ASP is a fully declarative language that allows for more robust and concise modeling of complex reasoning patterns, such as handling incomplete information or specific domain constraints, without needing to manage the procedural search strategies often required in Prolog.

How the ASP Approach Works

The core of the research involves creating a general method to encode CARCASS abstractions directly into ASP. By representing abstract states and actions as logical rules, the system can automatically determine which abstract state a current environment belongs to. This allows the agent to perform Q-learning—a standard reinforcement learning algorithm—on a much smaller, simplified version of the state-action space. Because the ASP encoding is highly expressive, it can easily incorporate background knowledge about the environment, which helps the agent generalize its learned behavior to new, related tasks more effectively.

Evaluating Performance

To test this approach, the researchers applied their ASP-based implementation to two distinct domains: the Blocks World, a classic planning problem involving stacking objects, and MiniGrid, a suite of navigation tasks that require sub-goals like opening doors or collecting keys. The results indicate that using ASP to manage these abstractions is a highly effective strategy. In both case studies, the agents were able to learn high-quality policies consistently. Notably, the use of these logical abstractions allowed the agents to reach their goals using significantly fewer training samples compared to learning without any abstraction.

Key Takeaways

The research demonstrates that combining the CARCASS framework with ASP provides a powerful, flexible tool for reinforcement learning. By leveraging domain knowledge through declarative logic, developers can create agents that are better at generalizing across large state spaces. This approach is particularly beneficial in scenarios where the environment is complex but the underlying rules governing the objects and their relations are well-understood. The authors have made their implementation and additional documentation publicly available to support further exploration of this method.