Joshua Romoff

According to our database, Joshua Romoff authored at least 19 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improving Intrinsic Exploration by Creating Stationary Objectives.

Roger Creus Castanyer, Joshua Romoff, Glen Berseth

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play.

Daniel Bairamian, Philippe Marcotte, Joshua Romoff, Gabriel Robert, Derek Nowrouzezahrai

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

Learning Computational Efficient Bots with Costly Features.

Anthony Kobanda, Valliappan C. A., Joshua Romoff, Ludovic Denoyer

Proceedings of the IEEE Conference on Games, 2023

2022

Direct Behavior Specification via Constrained Reinforcement Learning.

Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal

Proceedings of the International Conference on Machine Learning, 2022

2021

Graph augmented Deep Reinforcement Learning in the GameRLand3D environment.

Edward Beeching, Maxim Peter, Philippe Marcotte, Jilles Dibangoye, Olivier Simonin, Joshua Romoff, Christian Wolf

CoRR, 2021

Deep Reinforcement Learning for Navigation in AAA Video Games.

Eloi Alonso, Maxim Peter, David Goumard, Joshua Romoff

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

CoRR, 2020

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning.

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

CoRR, 2020

2019

Separating value functions across time-scales.

Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Emma Brunskill, Joelle Pineau

CoRR, 2019

Randomized Value Functions via Multiplicative Normalizing Flows.

Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning.

Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Separable value functions across time-scales.

Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill

Proceedings of the 36th International Conference on Machine Learning, 2019

TarMAC: Targeted Multi-Agent Communication.

Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods.

Peter Henderson, Joshua Romoff, Joelle Pineau

CoRR, 2018

Reward Estimation for Variance Reduction in Deep Reinforcement Learning.

Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Multi-Advisor Reinforcement Learning.

Romain Laroche, Mehdi Fatemi, Joshua Romoff, Harm van Seijen

CoRR, 2017

Hybrid Reward Architecture for Reinforcement Learning.

Harm van Seijen, Mehdi Fatemi, Romain Laroche, Joshua Romoff, Tavian Barnes, Jeffrey Tsang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Improving Scalability of Reinforcement Learning by Separation of Concerns.

Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche

CoRR, 2016