297 citations · 859 across the 24 of their papers we have counts for
13 papers · 1 filter
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…
: Policy Representations with Successor Features
Gianluca Scarpellini, Ksenia Konyushkova, Claudio Fantacci +3
This paper describes , a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of founda…
Active Offline Policy Selection
Ksenia Konyushkova, Yutian Chen, Tom Le Paine +5
This paper addresses the problem of policy selection in domains with abundant logged data, but with a restricted interaction budget. Solving this problem would enable safe evaluati…
On Instrumental Variable Regression for Deep Offline Policy Evaluation
Yutian Chen, Liyuan Xu, Caglar Gulcehre +4
We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression p…
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R. Zhang, Tom Le Paine, Ofir Nachum +4
Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action…
Benchmarks for Deep Off-Policy Evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum +10
Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making. The ability…
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…
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…
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…
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Tom Le Paine, Caglar Gulcehre, Bobak Shahriari +11
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial…
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang +8
Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap…
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden +3
Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domai…
Fast Generation for Convolutional Autoregressive Models
Prajit Ramachandran, Tom Le Paine, Pooya Khorrami +6
Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial…