Grid World

Grid worlds are simplified, grid-based environments used extensively in artificial intelligence research to model and test various algorithms, particularly in reinforcement learning and multi-agent systems. Current research focuses on improving the accuracy and efficiency of decision-making within these environments, employing techniques like Transformer-LSTM-PSO models for prediction and appraisal-guided Proximal Policy Optimization for simulating psychological processes. These studies contribute to a deeper understanding of agent behavior, decision-making under uncertainty, and the development of more robust and adaptable AI systems, with applications ranging from smart grid management to robotics and healthcare.