Mastering Atari with Discrete World Models: DreamerV2

In alliance with Deep Mind and the University of Toronto, Google has released DreamerV2, the very first Reinforcement Learning agent that achieves human-level Atari performance. The paper was released under this name: Mastering Atari with Discrete World Models by Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba. Reinforcement Learning methods have made quite a progress in a short time. These approaches have successfully beaten their respective world champions by using model-free learning methods to model-based methods.  DreamerV2, a model-based method in which the agent predicts the output of the potential actions performed to make informed decisions for a new scenario. The proposed method uses the Dreamer agent from DreamerV1 with a bit of adjustification. Using a single GPU and a single environment instance, DreamerV2 outperforms top model-free single-GPU agents within the same computational budget and training time.  The Model Architecture of Dreamer