Research into how artificial agents can choose actions to achieve goals is making rapid progress in large part due to the use of reinforcement learning (RL). Model-free approaches to RL, which learn to predict successful actions through trial and error, have enabled DeepMind's DQN to play Atari games and AlphaStar to beat world champions at Starcraft II, but require large amounts of environment interaction, limiting their usefulness for real-world scenarios.
In contrast, model-based RL approaches additionally learn a simplified model of the environment. This world model lets the agent predict the outcomes of potential action sequences, allowing it to play through hypothetical scenarios to make informed decisions in new situations, thus reducing the trial and error necessary to achieve goals. In the past, it has been challenging to learn accurate world models and leverage them to learn successful behaviors. While recent research, such as our Deep Planning Network (PlaNet), has pushed these boundaries by learning accurate world models from images, model-based approaches have still been held back by ineffective or computationally expensive planning mechanisms, limiting their ability to solve difficult tasks.
Today, in collaboration with DeepMind, we present Dreamer, an RL agent that learns a world model from images and uses it to learn long-sighted behaviors. Dreamer leverages its world model to efficiently learn behaviors via backpropagation through model predictions. By learning to compute compact model states from raw images, the agent is able to efficiently learn from thousands of predicted sequences in parallel using just one GPU. Dreamer achieves a new state-of-the-art in performance, data efficiency and computation time on a benchmark of 20 continuous control tasks given raw image inputs. To stimulate further advancement of RL, we are releasing the source code to the research community.
https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html
Paper: https://arxiv.org/abs/1912.01603
Blog: https://dreamrl.github.io/