Hands-on Alchemy: A Structured Task Distribution for Meta-Reinforcement Learning

Hands-on Alchemy: A Structured Task Distribution for Meta-Reinforcement Learning

Alchemy is a single-player 3D video game, whose environment is implemented in Unity. Here is a demonstration video of how Alchemy works.

This post is in continuation with our previous article about Alchemy, the very first benchmark on meta-Reinforcement Learning. Deepmind with the University of London has released an open-source benchmark environment for meta-RL : Alchemy: A structured task distribution for meta-reinforcement learning by Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, Gavin Buttimore, David P. Reichert, Neil Rabinowitz, Loic Matthey, Demis Hassabis, Alex Lerchner, and Matthew Botvinick.

Meta-Learning or Learning-to-learn requires a series of tasks that provide both complete knowledge of task(accessibility) and shared structure from the real-world point of view(interesting). Existing work on meta-RL offered either of interest ability or accessibility but Alchemy extended the boundaries by providing both. Alchemy is a single-player 3D video game, whose environment is implemented in Unity. Here is a demonstration video of how Alchemy works.

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