Plan2Explore: active model-building for self-supervised visual reinforcement learning

To operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly. Reinforcement learning has enabled artificial agents to solve complex tasks both in simulation and real world. However, it requires collecting large amounts of experience in the environment, and the agent learns only that particular task, much like a student memorizing a lecture without understanding. Self-supervised reinforcement learning has emerged as an alternative, where the agent only follows an intrinsic objective that is independent of any individual task, analogously to unsupervised representation learning. After experimenting with the environment without supervision, the agent builds an understanding of the environment, which enables it to adapt to specific downstream tasks more efficiently.