Intrinsic Motivation
Overview
In reinforcement learning, intrinsic motivation gives agents internal reasons to explore and learn when external rewards are sparse or absent. Common signals include novelty and state coverage, prediction error and learning progress, information gain, entropy, and empowerment. Researchers use these signals to train skills and world models, guide exploration in robotics and multi-agent systems, and support active learning. A central challenge is distinguishing useful, learnable novelty from noise while balancing broad coverage against focused, safe exploration that helps with downstream tasks.
Latest papers
39 papers5 papers in the last four weeks, down 17% on the four weeks before. 0.0% of all new papers.