It is crucial to quantify the uncertainty of these dynamics models when using them for planning.

Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Team-Nash equilibrium (TNE) predicts the outcomes of such coordinated interactions.

However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions.

Decision trees are a fundamental workhorse in machine learning. Their logical and hierarchical structure makes them easy to understand and their predictions easy to explain.