Intrinsic Reward

Intrinsic reward in reinforcement learning aims to enhance exploration and learning efficiency by providing agents with additional, internally generated rewards beyond external task rewards. Current research focuses on improving the design and implementation of intrinsic reward mechanisms, often leveraging prediction-based methods, model-based approaches, and integration with large language models to guide exploration and improve sample efficiency in complex environments. This work is significant because it addresses key limitations of reinforcement learning, particularly in sparse-reward scenarios, leading to more efficient and robust learning algorithms with potential applications in robotics, game playing, and other domains requiring autonomous decision-making.