Intrinsic Control

Intrinsic control focuses on enabling autonomous agents to learn and explore their environments without relying solely on external rewards, mirroring the intrinsic motivation observed in humans and animals. Current research emphasizes developing algorithms that maximize information gain, often using mutual information or entropy minimization as objectives, and employing model architectures like variational autoencoders and deep reinforcement learning agents with latent state-space models. This research is significant because it promises more efficient and adaptable agents capable of unsupervised skill acquisition and improved performance in complex, uncertain environments, with applications ranging from robotics to AI-driven content creation.