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05/29/2023
VA-learning as a more efficient alternative to Q-learning
In reinforcement learning, the advantage function is critical for policy...
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03/14/2023
Fast Rates for Maximum Entropy Exploration
We consider the reinforcement learning (RL) setting, in which the agent ...
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02/12/2021
Bootstrapped Representation Learning on Graphs
Current state-of-the-art self-supervised learning methods for graph neur...
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11/18/2020
Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning ...
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06/13/2020
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-su...
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06/01/2019
Neural Replicator Dynamics
In multiagent learning, agents interact in inherently nonstationary envi...
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06/14/2018
Maximum a Posteriori Policy Optimisation
We introduce a new algorithm for reinforcement learning called Maximum a...
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04/18/2018
A Study on Overfitting in Deep Reinforcement Learning
Recent years have witnessed significant progresses in deep Reinforcement...
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02/05/2018
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
In this work we aim to solve a large collection of tasks using a single ...
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06/30/2017
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametr...
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06/20/2017
Observational Learning by Reinforcement Learning
Observational learning is a type of learning that occurs as a function o...
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04/15/2017
The Reactor: A Sample-Efficient Actor-Critic Architecture
In this work we present a new reinforcement learning agent, called React...
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02/16/2016
Q(λ) with Off-Policy Corrections
We propose and analyze an alternate approach to off-policy multi-step te...
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10/19/2012
