meta_dsl_with_oorl.md Object-Oriented Reinforcement Learning in Mutable Ontologies with Self-Reflective Meta-DSL
Arthur M. Collé
- Introduction
1.1. Motivation and Objectives
Reinforcement learning has made significant strides in enabling agents to learn complex behaviors through interaction with their environment. However, traditional approaches often struggle in open-ended, dynamic environments where the optimal behavior and relevant features may change over time.