Ray Summit 2020

Deep Reinforcement Learning (DRL) has become the technology of choice when trying to autonomously learn optimal policies for agents interacting with each other in arbitrarily complex environments. RLlib - Ray's open source RL library - addresses the problems of finding well performing algorithms for a given environment, configuring and customizing them, as well as running these algos in large-scale, distributed workloads to yield good decision making policies. This talk focuses on features and algorithms recently added to RLlib and how you can use these to solve novel environment- and problem types. Topics include in particular: Model-based RL, a new exploration API for RLlib, PyTorch support, curiosity and intrinsic motivation, and contextual bandits.