Maze Environment

Maze environments serve as a versatile benchmark for evaluating various AI algorithms, particularly in navigation, planning, and reinforcement learning. Current research focuses on improving the efficiency and robustness of these algorithms, exploring architectures like transformers and recurrent neural networks, and investigating the impact of factors such as goal specification, memory limitations, and multi-agent coordination. This research is significant because it advances our understanding of AI capabilities in complex scenarios and has implications for real-world applications such as robotics, autonomous navigation, and explainable AI.