EXPIL: Explanatory Predicate Invention for Learning in Games

Understand

Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel in various games.

• However, the black-box nature of neural network models often hinders our ability to understand the reasoning behind the agent's actions.
• Recent research has attempted to address this issue by using the guidance of pretrained neural agents to encode logic-based policies, allowing for interpretable decisions.
• A drawback of such approaches is the requirement of large amounts of predefined background knowledge in the form of predicates, limiting its applicability and scalability.