The goal of reinforcement learning is to train agents that interact with their environment to solve complex tasks, with
real-world applications ranging from robotics, self-driving cars, real-time bidding and more. From one angle we need to
develop algorithms to solve tasks, but from another we need to build an RL environment that will mimic real-world
problems. As of now, there are a number of professional developed RL environments that researchers can use to develop
and compare RL algorithms. The main purpose of this workshop is to review the most common RL toolkits.
Workshop Outline:
Intro by Alina Vereshchaka
Defining & Solving Grid-world Environment by Utkarsh Behre
