MiniGrid Environment

MiniGrid is a simplified, grid-based environment commonly used in reinforcement learning research to study various aspects of agent behavior, such as exploration, navigation, and planning. Current research focuses on improving the scalability and efficiency of MiniGrid simulations, often leveraging frameworks like JAX for accelerated training on GPUs and TPUs, enabling faster experimentation with larger-scale and more complex tasks. This work is significant because it facilitates the development and evaluation of more sophisticated reinforcement learning models, ultimately advancing the field and potentially impacting applications in areas like robotics and autonomous systems. Furthermore, MiniGrid's adaptability allows researchers to explore diverse problems, including curriculum learning and compositional task generation.