Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Caglar Gulcehre Equal contributions DeepMind, London Tom Le Paine Equal contributions DeepMind, London Bobak Shahriari DeepMind, London Misha Denil DeepMind, London Matt Hoffman DeepMind, London Hubert Soyer DeepMind, London Richard Tanburn DeepMind, London Steven Kapturowski DeepMind, London Neil Rabinowitz DeepMind, London Duncan Williams DeepMind, London Gabriel Barth-Maron DeepMind, London Ziyu Wang DeepMind, London Nando de Freitas DeepMind, London Worlds Team DeepMind, London
Abstract
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks where other state of the art methods (both with and without demonstrations) fail to see even a single successful trajectory after tens of billions of steps of exploration.
原文 arXiv:1909.01387;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1909.01387v1