General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

110 Results

Oral
Tue 5:00 Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
Spotlight
Tue 5:20 Reinforcement Learning with Prototypical Representations
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Spotlight
Tue 5:25 Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Seungyul Han, Youngchul Sung
Spotlight
Tue 5:30 Muesli: Combining Improvements in Policy Optimization
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theo Weber, David Silver, Hado van Hasselt
Spotlight
Tue 5:35 Unsupervised Learning of Visual 3D Keypoints for Control
Boyuan Chen, Pieter Abbeel, Deepak Pathak
Spotlight
Tue 5:40 Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Spotlight
Tue 5:45 State Entropy Maximization with Random Encoders for Efficient Exploration
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee
Oral
Tue 7:00 Skill Discovery for Exploration and Planning using Deep Skill Graphs
Akhil Bagaria, Jason Senthil, George Konidaris
Oral
Tue 7:00 World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly Stadie
Spotlight
Tue 7:20 Learning Routines for Effective Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
Spotlight
Tue 7:20 Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Johan Obando Ceron, Pablo Samuel Castro
Spotlight
Tue 7:25 Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
Annie Xie, James Harrison, Chelsea Finn
Spotlight
Tue 7:25 PODS: Policy Optimization via Differentiable Simulation
Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros
Spotlight
Tue 7:30 Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
Spotlight
Tue 7:30 Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Amin Barekatain, Simon Schmitt, David Silver
Spotlight
Tue 7:35 Inverse Constrained Reinforcement Learning
Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed
Spotlight
Tue 7:35 Model-Based Reinforcement Learning via Latent-Space Collocation
Oleg Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine
Spotlight
Tue 7:40 Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Thomas Mesnard, Theo Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos
Spotlight
Tue 7:40 Vector Quantized Models for Planning
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals
Poster
Tue 9:00 Skill Discovery for Exploration and Planning using Deep Skill Graphs
Akhil Bagaria, Jason Senthil, George Konidaris
Poster
Tue 9:00 Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Thomas Mesnard, Theo Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos
Poster
Tue 9:00 State Entropy Maximization with Random Encoders for Efficient Exploration
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee
Poster
Tue 9:00 Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
Poster
Tue 9:00 Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Seungyul Han, Youngchul Sung
Poster
Tue 9:00 World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly Stadie
Poster
Tue 9:00 Model-Based Reinforcement Learning via Latent-Space Collocation
Oleg Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine
Poster
Tue 9:00 Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
Annie Xie, James Harrison, Chelsea Finn
Poster
Tue 9:00 Reinforcement Learning with Prototypical Representations
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Poster
Tue 9:00 Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Johan Obando Ceron, Pablo Samuel Castro
Poster
Tue 9:00 PODS: Policy Optimization via Differentiable Simulation
Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros
Poster
Tue 9:00 Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Amin Barekatain, Simon Schmitt, David Silver
Poster
Tue 9:00 Decoupling Value and Policy for Generalization in Reinforcement Learning
Roberta Raileanu, Rob Fergus
Poster
Tue 9:00 Learning Routines for Effective Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
Poster
Tue 9:00 Vector Quantized Models for Planning
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals
Poster
Tue 9:00 Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola
Poster
Tue 9:00 Muesli: Combining Improvements in Policy Optimization
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theo Weber, David Silver, Hado van Hasselt
Poster
Tue 9:00 Inverse Constrained Reinforcement Learning
Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed
Poster
Tue 9:00 Unsupervised Learning of Visual 3D Keypoints for Control
Boyuan Chen, Pieter Abbeel, Deepak Pathak
Poster
Tue 9:00 Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
Oral
Tue 17:00 PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
Spotlight
Tue 17:25 Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee
Spotlight
Tue 17:30 Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum
Spotlight
Tue 17:35 Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Elad Sarafian, Shai Keynan, Sarit Kraus
Spotlight
Tue 17:45 Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela Larma, Brenden Petersen, Sookyung Kim, Claudio Santiago, Ruben Glatt, Nathan Mundhenk, Jacob Pettit, Daniel Faissol
Oral
Tue 18:00 Decoupling Value and Policy for Generalization in Reinforcement Learning
Roberta Raileanu, Rob Fergus
Oral
Tue 18:00 PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Kimin Lee, Laura Smith, Pieter Abbeel
Spotlight
Tue 18:20 Prioritized Level Replay
Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Spotlight
Tue 18:20 Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Josh M Susskind, Jian Zhang, Russ Salakhutdinov, Hanlin Goh
Spotlight
Tue 18:25 Keyframe-Focused Visual Imitation Learning
Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman
Spotlight
Tue 18:25 SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Jim Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar
Spotlight
Tue 18:30 GMAC: A Distributional Perspective on Actor-Critic Framework
Daniel Nam, Younghoon Kim, Chan Park
Spotlight
Tue 18:35 Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng
Spotlight
Tue 18:35 Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev
Spotlight
Tue 18:45 Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Ethan Brooks, Janarthanan Rajendran, Richard Lewis, Satinder Singh
Spotlight
Tue 18:45 Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Oral
Tue 19:00 Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Unnat Jain, Raymond Yeh, Alex Schwing
Spotlight
Tue 19:20 On Proximal Policy Optimization's Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Russ Salakhutdinov, Pradeep Ravikumar
Spotlight
Tue 19:20 A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto, David Meger, Doina Precup
Spotlight
Tue 19:25 Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Gu
Spotlight
Tue 19:30 DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu
Spotlight
Tue 19:35 MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin D Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
Spotlight
Tue 19:40 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Spotlight
Tue 19:45 SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao SONG, Penghui Qi, Peng Peng, Zhenkun Tang, Wei Zhang, Weimin Li, Xiongjun Pi, Jujie He, Chao Gao, Haitao Long, Quan Yuan
Poster
Tue 21:00 Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Poster
Tue 21:00 PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Kimin Lee, Laura Smith, Pieter Abbeel
Poster
Tue 21:00 Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Elad Sarafian, Shai Keynan, Sarit Kraus
Poster
Tue 21:00 Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Gu
Poster
Tue 21:00 Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Josh M Susskind, Jian Zhang, Russ Salakhutdinov, Hanlin Goh
Poster
Tue 21:00 MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin D Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
Poster
Tue 21:00 Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Unnat Jain, Raymond Yeh, Alex Schwing
Poster
Tue 21:00 Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng
Poster
Tue 21:00 GMAC: A Distributional Perspective on Actor-Critic Framework
Daniel Nam, Younghoon Kim, Chan Park
Poster
Tue 21:00 Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum
Poster
Tue 21:00 Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee
Poster
Tue 21:00 On Proximal Policy Optimization's Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Russ Salakhutdinov, Pradeep Ravikumar
Poster
Tue 21:00 Prioritized Level Replay
Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Poster
Tue 21:00 SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Jim Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar
Poster
Tue 21:00 Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Ethan Brooks, Janarthanan Rajendran, Richard Lewis, Satinder Singh
Poster
Tue 21:00 SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao SONG, Penghui Qi, Peng Peng, Zhenkun Tang, Wei Zhang, Weimin Li, Xiongjun Pi, Jujie He, Chao Gao, Haitao Long, Quan Yuan
Poster
Tue 21:00 DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu
Poster
Tue 21:00 Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela Larma, Brenden Petersen, Sookyung Kim, Claudio Santiago, Ruben Glatt, Nathan Mundhenk, Jacob Pettit, Daniel Faissol
Poster
Tue 21:00 A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto, David Meger, Doina Precup
Poster
Tue 21:00 PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
Poster
Tue 21:00 Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev
Poster
Tue 21:00 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Poster
Tue 21:00 Keyframe-Focused Visual Imitation Learning
Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman
Oral
Wed 5:00 Cross-domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit Roy-Chowdhury
Oral
Wed 5:00 APS: Active Pretraining with Successor Features
Hao Liu, Pieter Abbeel
Spotlight
Wed 5:20 SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel
Spotlight
Wed 5:20 Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert
Spotlight
Wed 5:25 Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Evan Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
Spotlight
Wed 5:30 Unsupervised Skill Discovery with Bottleneck Option Learning
Jaekyeom Kim, Seohong Park, Gunhee Kim
Spotlight
Wed 5:30 Active Feature Acquisition with Generative Surrogate Models
Yang Li, Junier Oliva
Spotlight
Wed 5:40 Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu
Spotlight
Wed 5:40 Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Austin W. Hanjie, Victor Zhong, Karthik Narasimhan
Spotlight
Wed 5:45 Data-efficient Hindsight Off-policy Option Learning
Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala Bukkapatnam, Noah Siegel, Nicolas Heess, Martin Riedmiller
Spotlight
Wed 5:45 Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Jimmy Yang, Justinian Rosca, Karthik Narasimhan, Peter Ramadge
Spotlight
Wed 6:20 Megaverse: Simulating Embodied Agents at One Million Experiences per Second
Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun
Poster
Wed 9:00 Cross-domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit Roy-Chowdhury
Poster
Wed 9:00 Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert
Poster
Wed 9:00 Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Austin W. Hanjie, Victor Zhong, Karthik Narasimhan
Poster
Wed 9:00 Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu
Poster
Wed 9:00 APS: Active Pretraining with Successor Features
Hao Liu, Pieter Abbeel
Poster
Wed 9:00 Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Evan Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
Poster
Wed 9:00 Megaverse: Simulating Embodied Agents at One Million Experiences per Second
Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun
Poster
Wed 9:00 Active Feature Acquisition with Generative Surrogate Models
Yang Li, Junier Oliva
Poster
Wed 9:00 SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel
Poster
Wed 9:00 Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Jimmy Yang, Justinian Rosca, Karthik Narasimhan, Peter Ramadge
Poster
Wed 9:00 Unsupervised Skill Discovery with Bottleneck Option Learning
Jaekyeom Kim, Seohong Park, Gunhee Kim
Poster
Wed 9:00 Data-efficient Hindsight Off-policy Option Learning
Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala Bukkapatnam, Noah Siegel, Nicolas Heess, Martin Riedmiller