Generative Data Intelligence

Reinforcement learning with unsupervised auxiliary tasks

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The combination of these auxiliary tasks, together with our previous A3C paper is our new UNREAL agent (UNsupervised REinforcement and Auxiliary Learning). We tested this agent on a suite of 57 Atari games as well as a 3D environment called Labyrinth with 13 levels. In all the games, the same UNREAL agent is trained in the same way, on the raw image output from the game, to produce actions to maximise the score or reward of the agent in the game. The behaviour required to get game rewards is incredibly varied, from picking up apples in 3D mazes to playing Space Invaders – the same UNREAL algorithm learns to play these games often to human level and beyond. Some results and visualisations can be seen in the video below.

Source: https://deepmind.com/blog/article/reinforcement-learning-unsupervised-auxiliary-tasks

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