94 citations · 399 across the 21 of their papers we have counts for
15 papers · 1 filter
Genie: Generative Interactive Environments
Jake Bruce, Michael Dennis, Ashley Edwards +22
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless…
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan +21
StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires…
Robust Learning-Augmented Caching: An Experimental Study
Jakub Chłędowski, Adam Polak, Bartosz Szabucki +1
Effective caching is crucial for the performance of modern-day computing systems. A key optimization problem arising in caching -- which item to evict to make room for a new item -…
Regularized Behavior Value Estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang +7
Offline reinforcement learning restricts the learning process to rely only on logged-data without access to an environment. While this enables real-world applications, it also pose…
Semi-supervised reward learning for offline reinforcement learning
Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar +4
In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such a…
Offline Learning from Demonstrations and Unlabeled Experience
Konrad Zolna, Alexander Novikov, Ksenia Konyushkova +6
Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. Howev…
Hyperparameter Selection for Offline Reinforcement Learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi +5
Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios. However, existing hyperparameter selection m…
Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna +8
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…
Combating False Negatives in Adversarial Imitation Learning
Konrad Zolna, Chitwan Saharia, Leonard Boussioux +4
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior. However, as the trained…
Task-Relevant Adversarial Imitation Learning
Konrad Zolna, Scott Reed, Alexander Novikov +6
We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. W…
Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift
Michał Zając, Konrad Zolna, Stanisław Jastrzębski
Recent work has shown that using unlabeled data in semi-supervised learning is not always beneficial and can even hurt generalization, especially when there is a class mismatch bet…
Reinforced Imitation in Heterogeneous Action Space
Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio +2
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an…
Classifier-agnostic saliency map extraction
Konrad Zolna, Krzysztof J. Geras, Kyunghyun Cho
Currently available methods for extracting saliency maps identify parts of the input which are the most important to a specific fixed classifier. We show that this strong dependenc…
Improving the Performance of Neural Networks in Regression Tasks Using Drawering
Konrad Zolna
The method presented extends a given regression neural network to make its performance improve. The modification affects the learning procedure only, hence the extension may be eas…