jkinrightLanguageRL

jkinrightLanguageRL is a theoretical programming language designed for research into reinforcement learning algorithms. It aims to provide a simplified and abstract environment for developing and testing novel RL agents and learning strategies. The language's core design focuses on enabling rapid prototyping and experimentation with different agent architectures, reward functions, and exploration techniques.

The syntax of jkinrightLanguageRL is intended to be declarative and high-level, abstracting away much of the low-level computational details often found in general-purpose programming languages. This allows researchers to focus on the conceptual aspects of reinforcement learning rather than intricate implementation challenges. Key features include built-in constructs for defining environments, agents, policies, and value functions.

While not a widely adopted production language, jkinrightLanguageRL serves as a valuable tool in academic and research settings. Its purpose is to facilitate the exploration of the theoretical underpinnings of artificial intelligence, specifically within the domain of learning through interaction and feedback. The language's abstraction level helps in isolating variables and understanding the impact of specific algorithmic choices on learning performance. Its development is primarily driven by the need for a flexible and intuitive platform for RL research.