unsupervised-reinforcement-learning

Unsupervised Reinforcement Learning (URL) is a variant of reinforcement learning where agents are designed to explore environments and learn from their experiences without explicit rewards or guidance. This approach can help agents discover useful strategies autonomously through trial and error. A common misconception about URL is that it is entirely devoid of any feedback; in reality, agents may still receive intrinsic rewards based on their exploration success, such as novelty or surprise.