Dopamine, a framework for experimenting with reinforcement learning (RL), has reached the 2.0 mark, now allowing the use of custom environments – just half a year after its initial launch. The project is based on popular numerical computation library TensorFlow and stems from a team of researchers at Google, though it isn't an official product of the company. It was meant for speculative research purposes and focuses on providing only a few heavily tested RL algorithms in an easy to use way. That is why for the first iteration the framework only included a single-GPU agent with implementations of n-step Bellman updates, prioritized experience replay, distributional reinforcement learning, and the Deep Q-Networks algorithm. According to a paper by members of the DeepMind team, which is also part of the Alphabet family, those approaches belong to the most important components of state-of-the-art reinforcement learning systems.