Search Results for author: John Quan

Found 15 papers, 7 papers with code

Podracer architectures for scalable Reinforcement Learning

no code implementations13 Apr 2021 Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt

Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally associated with production systems. Deep learning frameworks such as TensorFlow, PyTorch and JAX allow users to transparently make use of accelerators, such as TPUs and GPUs, to offload the more computationally intensive parts of training and inference in modern deep learning systems.

Towards Consistent Performance on Atari using Expert Demonstrations

no code implementations ICLR 2019 Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.

Atari Games

Recurrent Experience Replay in Distributed Reinforcement Learning

no code implementations ICLR 2019 Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan, Remi Munos

Using a single network architecture and fixed set of hyperparameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and surpasses the state of the art on DMLab-30.

Atari Games

Observe and Look Further: Achieving Consistent Performance on Atari

1 code implementation29 May 2018 Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.

Montezuma's Revenge

Distributed Prioritized Experience Replay

13 code implementations ICLR 2018 Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible.

Atari Games

Distral: Robust Multitask Reinforcement Learning

no code implementations NeurIPS 2017 Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu

Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.

Transfer Learning

Deep Q-learning from Demonstrations

3 code implementations12 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.

Decision Making Imitation Learning +1

Prioritized Experience Replay

63 code implementations18 Nov 2015 Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.

Atari Games

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