Search Results for author: John Agapiou

Found 3 papers, 2 papers with code

StarCraft II: A New Challenge for Reinforcement Learning

11 code implementations • 16 Aug 2017 • Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.

Ranked #1 on Starcraft II on MoveToBeacon

reinforcement-learning Reinforcement Learning (RL) +2

7,990

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Deep Q-learning from Demonstrations

5 code implementations • 12 Apr 2017 • Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys

We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.

Imitation Learning Q-Learning +1

2,946

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Strategic Attentive Writer for Learning Macro-Actions

no code implementations • NeurIPS 2016 • Alexander, Vezhnevets, Volodymyr Mnih, John Agapiou, Simon Osindero, Alex Graves, Oriol Vinyals, Koray Kavukcuoglu

We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting.

Atari Games

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