Google, OpenAI & DeepMind: Shared Task Behaviour Priors Can Boost RL and Generalization

Researchers in recent years have deployed reinforcement learning (RL) agents to solve increasingly challenging problems. As the trend continues, so has the development of new methods that enable the injection of "priors" (prior knowledge) into agents to help them better understand the structure of the world and come up with more effective solution strategies. In a new paper, researchers from Google, OpenAI, and DeepMind introduce "behaviour priors," a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts. The researchers discuss how such behaviour patterns can be captured using probabilistic trajectory models and how they can be integrated effectively into RL schemes, such as for facilitating multi-task and transfer learning. Their method for learning behaviour priors can lead to significant speedups on complex tasks, the researchers say.