Pseudo Goal

Pseudo-goals, in the context of artificial intelligence, refer to intermediate or surrogate objectives used to guide agents towards a final goal, often addressing challenges like sparse rewards, complex environments, or multi-agent coordination. Current research focuses on developing algorithms and models, including reinforcement learning with various divergence measures and graph neural networks, to efficiently generate and utilize pseudo-goals for improved performance in diverse tasks such as robot navigation, combinatorial optimization, and game playing. This research is significant for advancing AI capabilities in complex scenarios and has implications for various applications, including robotics, autonomous systems, and human-computer interaction.