Organizers

Accepted Papers

• "Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms" by Matthia Sabatelli (University of Liege)*; Gilles Louppe (University of Liège); Pierre Geurts (University of Liège); Marco Wiering (University of Groningen)
• "Single Deep Counterfactual Regret Minimization" by Eric Steinberger (University of Cambridge)*
• "rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch" by Adam Stooke (UC Berkeley)*; Pieter Abbeel (UC Berkeley)
• "Reinforcement Learning for High-dimensional Continuous Control in Biomechanical Systems: An Intro to ArtiSynth-RL" by Amir H Abdi (University of British Columbia)*
• "Adversarial Policies: Attacking Deep Reinforcement Learning" by Adam R Gleave (UC Berkeley)*; Michael Dennis (University of California Berkeley); Neel Kant (UC Berkeley); Cody Wild (University of California Berkeley); Sergey Levine (UC Berkeley); Stuart Russell (UC Berkeley)
• "Recurrent neural-linear posterior sampling for non-stationary bandits" by Paulo Rauber (IDSIA)*; Aditya Ramesh (USI); Jürgen Schmidhuber (IDSIA - Lugano)
• "LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games" by Leonard Adolphs (ETHZ)*; Thomas Hofmann (ETH Zurich)
• "Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware" by Kirill Polzounov (University of Calgary)*; Ramitha Sundar (Blue River Technology); Lee Reden (Blue River Technology)
• "Deep Model Predictive Control with SafetyAugmented Value Estimation from Demonstrations" by Ashwin Balakrishna (UC Berkeley)*; Brijen Thananjeyan (UC Berkeley); Ugo Rosolia (UC Berkeley); Felix Li (UC Berkeley); Joseph Gonzalez (UC Berkeley); Sergey Levine (UC Berkeley); Francesco Borrelli (UC Berkeley); Ken Goldberg (UC Berkeley)
• "Behaviour Suite for Reinforcement Learning" by Ian Osband (Deep Mind Google)*
• "Accelerating Training in Pommerman with Imitation and Reinforcement Learning" by Hardik Meisheri (TCS Research)*; Omkar Shelke (TCS Research); Richa Verma (TCS Research); Harshad Khadilkar (TCS Research)
• "Anonymous Research Football: A Novel Reinforcement Learning Environment" by Karol Kurach (Google Brain)*; Anton Raichuk (Google); Piotr Stańczyk (Google Brain); Michał Zając (Google Brain); Olivier Bachem (Google Brain); Lasse Espeholt (DeepMind); Carlos Riquelme (Google Brain); Damien Vincent (Google Brain); Marcin Michalski (Google); Olivier Bousquet (Google); Sylvain Gelly (Google Brain)
• "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics" by Johannes Ackermann (Technical University of Munich)*; Volker Gabler (Technical University of Munich); Takayuki Osa (Kyushu Institute of Technology); Masashi Sugiyama (RIKEN/The University of Tokyo)
• "Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning" by Matthew Benatan (IBM Research)*; Edward Pyzer-Knapp (IBM Research)
• "Is Deep Reinforcement Learning Really Superhuman on Atari?" by Marin Toromanoff (Mines ParisTech)*; Emilie Wirbel (Valeo); Fabien Moutarde (Mines ParisTech)
• "Harnessing Structures for Value-Based Planning and Reinforcement Learning" by Guo Zhang (MIT)*; Yuzhe Yang (MIT); Zhi Xu (MIT); Dina Katabi (Massachusetts Institute of Technology)
• "Conservative Policy Gradient: Reducing Variance and Instability in Off-Policy Reinforcement Learning" by Chen Tessler (Technion)*; Nadav Merlis (Technion); Shie Mannor (Technion)
• "Nested-Wasserstein Self-Imitation Learning for Sequence Generation" by Ruiyi Zhang (Duke University)*; Changyou Chen (University at Buffalo); Zhe Gan (Microsoft); Zheng Wen (DeepMind); Wenlin Wang (Duke Univeristy); Lawrence Carin (Duke University)
• "The StarCraft Multi-Agent Challenge" by Mikayel Samvelyan (Russian-Armenian University)*; Tabish Rashid (University of Oxford); Christian Schroeder de Witt (University of Oxford); Gregory Farquhar (University of Oxford); Nantas Nardelli (University of Oxford); Tim G. J. Rudner (University of Oxford); Chia-Man Hung (University of Oxford); Philip Torr (University of Oxford); Jakob Foerster (University of Oxford); Shimon Whiteson (University of Oxford)
• "Marginalized State Distribution Entropy Regularization in Policy Optimization" by Riashat Islam (MILA, Mcgill University)*; Zafarali Ahmed (MILA, McGill University); Doina Precup (McGill University)
• "Skew-Fit: State-Covering Self-Supervised Reinforcement Learning" by Murtaza Dalal (UC Berkeley)*; Vitchyr H Pong (UC Berkeley); Steven Lin (UC Berkeley); Ashvin V Nair (UC Berkeley); Shikhar Bahl (UC Berkeley); Sergey Levine (UC Berkeley)
• "DRIFT: Deep Reinforcement Learning for Functional Software Testing" by Luke Harries (Microsoft); Rebekah Clarke (Microsoft); Timothy Chapman (Microsoft); Swamy Nallamalli (Microsoft); Levent Ozgur (Microsoft); Shuktika Jain (Microsoft ); Alex Leung (Microsoft); Steve Lim (Microsoft ); Aaron Dietrich (Microsoft ); Jose Miguel Hernandez-Lobato (University of Cambridge); Tom Ellis (Microsoft); Cheng Zhang (Microsoft)*; Kamil Ciosek (Microsoft)
• "AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers" by Andrey Kurenkov (Stanford University)*; Ajay Mandlekar (Stanford University); Roberto Martín-Martín (Stanford University); Animesh Garg (Stanford, Nvidia)
• "Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning" by Kevin Sebastian Luck (Arizona State University)*; Heni Ben Amor (Arizona State University); Roberto Calandra (Facebook)
• "Prioritized Sequence Experience Replay" by Marc W Brittain (Iowa State University)*; Joshua R Bertram (Iowa State University); Xuxi Yang (Iowa State University); Peng Wei (Iowa State University)
• "SpECTra : Sparse Entity-Centric Transitions" by Rim Assouel (umontreal.ca)*
• "Task-Relevant Adversarial Imitation Learning" by Konrad Tomasz Żołna (Jagiellonian University)*; Scott Reed (DeepMind); Ziyu Wang (-); Alexander Novikov (DeepMind); David Budden (DeepMind); Serkan Cabi (DeepMind); Sergio Gómez Colmenarejo (DeepMind); Misha Denil (DeepMind); Nando de Freitas (DeepMind)
• "Dynamic Vehicle Dispatching Based on Minimum Fleet A Deep Reinforcement Learning Method" by Zhang Wenqi (Beijing University of Posts and Telecommunications); Qiang Wang (Beijing university of Posts and telecommunications)*; jingjing li (Beijing University of Posts and Telecommunications); Donghai Shi (Didi Chuxing)
• "RoboNet: Learning and Generalizing across Robots through Large-Scale Visual Prediction and Planning" by Sudeep Dasari (UC Berkeley)*; Frederik D Ebert (UC Berkeley); Stephen Tian (UC Berkeley); Chelsea Finn (Stanford); Sergey Levine (UC Berkeley)
• "DisCoRL: Continual Reinforcement Learning via Policy Distillation" by René Traoré (ENSTA ParisTech); Hugo Caselles-Dupré (Flowers Team (ENSTA ParisTech & INRIA) & Softbank Robotics Europe)*; Timothee Lesort (Ensta Paristech); Te Sun (ENSTA ParisTech); Guanghang Cai (ENSTA ParisTech); Natalia Diaz Rodriguez (ENSTA ParisTech); David Filliat (ENSTA)
• "Dynamical Distance Learning for Unsupervised and Semi-Supervised Skill Discovery" by Kristian Hartikainen (UC Berkeley)*; XINYANG GENG (UC Berkeley); Tuomas Haarnoja (DeepMind); Sergey Levine (UC Berkeley)
• "Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?" by Ofir Nachum (Google)*; Haoran Tang (University of California Berkeley); Xingyu Lu (Berkeley); Shixiang Gu (Google Brain); Honglak Lee (Google); Sergey Levine (UC Berkeley)
• "A Simple Randomization Technique for Generalization in Deep Reinforcement Learning" by Kimin Lee (KAIST)*; Kibok Lee (University of Michigan); Jinwoo Shin (KAIST); Honglak Lee (University of Michingan Ann Arbor, USA)
• "On the Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER" by Markus Holzleitner (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); José Arjona-Medina (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Marius-Constantin Dinu (LIT AI Lab / University Linz ); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)*
• "Controlling Quantum Dot Devices using Deep Reinforcement Learning" by Vu Nguyen (University of Oxford)*; Dominic T Lennon (University of Oxford); Hyungil Moon (University of Oxford); Nina M. van Esbroeck (University of Oxford); Dino Sejdinovic (University of Oxford); Michael A. Osborne (University of Oxford); G. Andrew D. Briggs (University of Oxford); Natalia Ares (University of Oxford)
• "V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control" by Francis Song (DeepMind)*; Abbas Abdolmaleki (Google DeepMind); Jost Tobias Springenberg (DeepMind); Aidan Clark (DeepMind); Hubert Soyer (); Jack Rae (Deepmind); Seb Noury (); Arun Ahuja (DeepMind); Siqi Liu (DeepMind); Dhruva Tirumala (DeepMind); Nicolas Heess (DeepMind); Dan Belov (DeepMind); Martin Riedmiller (DeepMind); Matthew Botvinick (google)
• "Learning Independently-Obtainable Reward Functions" by Christopher Grimm (University of Michigan)*; Satinder Singh (UMich)
• "Self-Imitation Learning of Locomotion Movements through Termination Curriculum" by Amin Babadi (Aalto University)*; Kourosh Naderi (Aalto University); Perttu Hämäläinen (Aalto University)
• "Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents" by Felipe Leno da Silva (University of Sao Paulo)*; Pablo Hernandez-Leal (Borealis AI); Bilal Kartal (Borealis AI); Matthew Taylor (Borealis AI)
• "Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling" by Yuping Luo (Princeton University)*; Huazhe Xu (UC Berkeley); Tengyu Ma (Stanford University)
• "Behavior-Regularized Offline Reinforcement Learning" by Yifan Wu (Carnegie Mellon University)*; George Tucker (Google Brain); Ofir Nachum (Google)
• "Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model" by Alex X Lee (UC Berkeley)*; Anusha Nagabandi (UC Berkeley); Pieter Abbeel (UC Berkeley); Sergey Levine (UC Berkeley)
• "Making Efficient Use of Demonstrations to Solve Hard Exploration Problems" by Thomas L Paine (DeepMind)*; Caglar Gulcehre (DeepMind); Bobak Shahriari (Deepmind); Misha Denil (DeepMind); Matt Hoffman (DeepMind); Hubert Soyer (DeepMind); Richard Tanburn (Deepmind); Steven Kapturowski (Deepmind); Neil Rabinowitz (DeepMind); Duncan Williams (Deepmind); Gabriel Barth-Maron (DeepMind); Ziyu Wang (DeepMind); Nando de Freitas (DeepMind)
• "Offline Reinforcement Learning via Trajectory Synthesis" by Wei-Yang Qu (Nanjing University); Yang Yu (Nanjing University); Qingyang Li (Didi Research America)*; Zhiwei Qin (Didi Research America); Mengyue Yang (AI Labs, Didi Chuxing); Yiping Meng (Didi Chuxing); Jieping Ye (Didi Chuxing)
• "Learning an off-policy predictive state representation for deep reinforcement learning for vision-based steering in autonomous driving" by Daniel Graves (Huawei)*
• "MERL: Multi-Head Reinforcement Learning" by Yannis Flet-Berliac (University of Lille / Inria)*; Philippe Preux (INRIA)
• "Combining Model-based and Model-free Reinforcement Learning through Evolution" by Yunhao Tang (Columbia University)*
• "Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation" by Suraj Nair (Stanford University)*; Chelsea Finn (Google Brain)
• "Biologically inspired architectures for sample-efficient deep reinforcement learning" by Pierre H Richemond (Imperial College)*; Arinbjorn Kolbeinsson (Imperial College); Yike Guo (Imperial College London)
• "Interactive Fiction Games: A Colossal Adventure" by Matthew Hausknecht (Microsoft Research)*; Prithviraj V Ammanabrolu (Georgia Institute of Technology); Marc-Alexandre Côté (Microsoft Research); Xingdi Yuan (Microsoft Research)
• "Option Discovery using Deep Skill Chaining" by Akhil Bagaria (Brown University)*; George Konidaris (Brown University)
• "Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real" by Ofir Nachum (Google)*; Michael Ahn (Google); Hugo Ponte (Self); Shixiang Gu (Google Brain); vikash kumar (Google)
• "Curiosity-Driven Multi-Criteria Hindsight Experience Replay" by John B Lanier (UC Irvine)*; Stephen Mcaleer (Intel AI); Pierre Baldi (UC Irvine)
• "The PlayStation Reinforcement Learning Environment (PSXLE)" by Carlos R Purves (University of Cambridge)*; Cătălina Cangea (University of Cambridge); Petar Veličković (DeepMind)
• "Off-Policy Actor-Critic with Shared Experience Replay" by Simon Schmitt (DeepMind)*; Karen Simonyan (DeepMind); Matteo Hessel (DeepMind)
• "Emergent Tool Use from Multi-Agent Autocurricula" by Bowen Baker (OpenAI)*; Ingmar Kanitscheider (OpenAI); Todor Markov (OpenAI); Yi Wu (UC Berkeley); Glenn Powell (OpenAI); Bob McGrew (OpenAI); Igor Mordatch (OpenAI)
• "A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme" by Paavo Parmas (Okinawa Inst. of Sci. and Tech)*; Masashi Sugiyama (RIKEN/The University of Tokyo)
• "Objective Mismatch in Model-based Reinforcement Learning" by Nathan O Lambert (UC Berkeley)*; Brandon Amos (Facebook); Omry Yadan (Facebook); Roberto Calandra (Facebook)
• "If MaxEnt RL is the Answer, What is the Question?" by Ben Eysenbach (Carnegie Mellon University)*; Sergey Levine (UC Berkeley)
• "Efficient Exploration via State Marginal Matching" by Lisa Lee (Carnegie Mellon University); Ben Eysenbach (Carnegie Mellon University)*; Emilio Parisotto (Carnegie Mellon University); Eric Xing (Petuum Inc. and CMU); Sergey Levine (UC Berkeley); Ruslan Salakhutdinov (Carnegie Mellon University)
• "Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning" by Qian Long (CMU); Zihan Zhou (SJTU); Abhinav Gupta (CMU/FAIR); Fei Fang (Carnegie Mellon University); Yi Wu (UC Berkeley); Xiaolong Wang (CMU)*
• "Optimal Liquidation with Deep Reinforcement Learning" by Siyu Lin (University of Virginia)*; Peter Beling (University of Virginia)
• "ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations" by Daniel T Seita (University of California, Berkeley)*; David Chan (University of California, Berkeley); Roshan M Rao (UC Berkeley); Chen Tang (UC Berkeley); Mandi Zhao (UC Berkeley); John F Canny (UC Berkeley)
• "Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning" by Abhishek Gupta (UC Berkeley)*; vikash kumar (Google); Corey Lynch (Google); Sergey Levine (UC Berkeley); Karol Hausman (Google)
• "QXplore: Q-learning Exploration by Maximizing Temporal Difference Error" by Riley W Simmons-Edler (Princeton University)*; Benjamin Eisner (Samsung Research America); Eric Mitchell (Samsung AI Center NYC); H. Sebastian Seung (Princeton University); Daniel Lee (Cornell University)
• "Imitation Learning via Off-Policy Distribution Matching" by Ilya Kostrikov (Google/New York University)*; Ofir Nachum (Google); Jonathan Tompson (Google)
• "Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy" by Yijie Guo (University of Michigan)*; Jongwook Choi (University of Michigan); Marcin Moczulski (Google Brain); Samy Bengio (Google Research, Brain Team); Mohammad Norouzi (Google Brain); Honglak Lee (Google)
• "Temporal-difference learning for nonlinear value function approximation in the lazy training regime" by Andrea Agazzi (Duke University)*; Jianfeng Lu (Duke University)
• "Decentralized Multi-Agent Actor-Critic with Generative Inference" by Kevin M Corder (University of Delaware)*; Manuel Vindiola (Nil); Keith Decker (University of Delaware)
• "ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots" by Vikash Kumar (Google)*; Michael Ahn (Google); Abhishek Gupta (UC Berkeley); Sergey Levine (UC Berkeley); Henry Zhu (UC Berkeley); Kristian Hartikainen (UC Berkeley); Hugo Ponte
• "On Learning Symmetric Locomotion" by Farzad Abdolhosseini (University of British Columbia); Hung Yu Ling (University of British Columbia)*; Zhaoming Xie (University of British Columbia); Xue Bin Peng (UC Berkeley); Michiel van de Panne (University of British Columbia)
• "Benchmarking Batch Deep Reinforcement Learning Algorithms" by Scott Fujimoto (McGill University)*; Edoardo Conti (Facebook); Mohammad Ghavamzadeh (Facebook); Joelle Pineau (McGill / Facebook)
• "Search in Cooperative Partially Observable Games" by Adam Lerer (Facebook AI Research); Hengyuan Hu (Facebook); Jakob Foerster (Facebook AI Research); Noam Brown (Facebook AI Research)*
• "Asynchronous Methods for Model-Based Reinforcement Learning" by Ignasi Clavera (UC Berkeley); Yunzhi Zhang (UC Berkeley)*; Boren Tsai (UC Berkeley); Pieter Abbeel (UC Berkeley)
• "Deep Imitative Models for Flexible Inference, Planning, and Control" by Nicholas Rhinehart (CMU)*; Rowan McAllister (UC Berkeley); Sergey Levine (UC Berkeley)
• "Real-World Vision-Based Skill Learning with Context-Conditioned Goal Setting" by Ashvin V Nair (UC Berkeley)*; Shikhar Bahl (UC Berkeley); Khazatsky Alexander (UC Berkeley); Vitchyr H Pong (UC Berkeley); Glen Berseth (University of California Berkeley); Sergey Levine (UC Berkeley)
• "Multiplayer AlphaZero" by Nicholas Petosa (Georgia Institute of Technology)*; Tucker Balch (Ga Tech)
• "Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards" by Gerrit Schoettler (Siemens); Ashvin V Nair (UC Berkeley)*; Jianlan Luo (UC Berkeley); Shikhar Bahl (UC Berkeley); Juan Aparicio Ojea (Siemens); Eugen Solowjow (Siemens); Sergey Levine (UC Berkeley)
• "Plan2Vec: Unsupervised Representation Learningby Latent Plans" by Ge Yang (University of Chicago)*; Amy Zhang (FAIR, McGill); Ari S Morcos (Facebook AI Research (FAIR)); Joelle Pineau (McGill / Facebook); Pieter Abbeel (UC Berkeley); Roberto Calandra (Facebook)
• "Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift" by Riashat Islam (MILA, Mcgill University)*; Deepak Sharma (McGill University); Komal Teru (McGill University)
• "Neural Policy Gradient Methods: Global Optimality and Rates of Convergence" by Lingxiao Wang (Northwestern University)*; Qi Cai (Northwestern University); Zhuoran Yang (Princeton.edu); Zhaoran Wang (Northwestern U)
• "Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies" by Sungryull Sohn (University of Michigan)*; Hyunjae Woo (university of michigan); Jongwook Choi (University of Michigan); Honglak Lee (University of Michingan Ann Arbor, USA)
• "Learning Sparse Representations Incrementally in Deep Reinforcement Learning" by J. Hernandez-Garcia (University of Alberta)*; Richard Sutton (University of Alberta)
• "3D macromolecule localization in cryo-electrontomography with deep reinforcement learning" by Yaohui Cai (Peking University); Xiangrui Zeng (Carnegie Mellon University); Yuchen Zeng (Pennsylvania State University); Weilin Liu (Tsinghua University); Jie Jin (Chinese Academy of Sciences); Zachary Freyberg (University of Pittsburgh); Ge Yang (Chinese Academy of Sciences); Min Xu (Carnegie Mellon University)*
• "Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination" by Shauharda Khadka (Intel AI Lab)*; Somdeb Majumdar (Intel AI Lab); Santiago Miret (Intel AI Lab); Stephen McAleer (Intel AI Lab); Kagan Tumer (Oregon State University US)
• "Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents" by Xian Yeow Lee (Iowa State University)*; Sambit Ghadai (Iowa State University); Kai Liang Tan (Iowa State University); Chinmay Hegde (Iowa State University); Soumik Sarkar (Iowa State University)
• "Upside-Down Reinforcement Learning: Don’t Predict Rewards -- Just Map them to Actions" by Rupesh K Srivastava (NNAISENSE)*; Pranav Shyam (NNAISENSE); Filipe Mutz (IFES/UFES); Wojciech Jaśkowski (NNAISENSE SA); Jürgen Schmidhuber (IDSIA - Lugano)
• "Options of Interest: Temporal Abstraction with Interest Functions" by Khimya Khetarpal (McGill University)*; Martin Klissarov (McGill); Maxime Chevalier-Boisvert (Mila, Université de Montréal); Pierre-Luc Bacon (Stanford University); Doina Precup (McGill University)
• "Adaptive Planning Horizon for Model-Based Reinforcement Learning" by Chenjun Xiao (University of Alberta)*; Yifan Wu (Carnegie Mellon University); Dale Schuurmans (Google / University of Alberta); Martin Müller (University of Alberta)
• "Object Abstraction in Visual Model-Based Reinforcement Learning" by Rishi Veerapaneni (UC Berkeley)*; John D Co-Reyes (UC Berkeley); Michael Chang (University of California, Berkeley); Chelsea Finn (UC Berkeley); Sergey Levine (UC Berkeley); Michael Janner (); Jiajun Wu (MIT); Joshua Tenenbaum (MIT)
• "Bottom-Up Meta-Policy Search" by Luckeciano C Melo (Aeronautics Institute of Technology)*
• "Striving for Simplicity in Off-Policy Deep Reinforcement Learning" by Rishabh Agarwal (Google Research, Brain Team)*; Dale Schuurmans (Google / University of Alberta); Mohammad Norouzi (Google Brain)
• "Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning" by Tianhe Yu (Stanford University)*; Deirdre Quillen (Self); Zhanpeng He (University of Southern California); Ryan Julian (); Karol Hausman (Google Brain); Sergey Levine (UC Berkeley); Chelsea Finn (UC Berkeley)
• "Adaptive Temperature Tuning for Mellowmax in Deep Reinforcement Learning" by Seungchan Kim (Brown University)*; George Konidaris (Brown)
• "Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation" by Glen Berseth (University of California Berkeley)*; Brandon Haworth (York University); Mubbasir Kapadia (Rutgers University); Petros Faloutsos (York University)
• "Intelligent Coordination Among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning" by Ujwal Padam Tewari (Siemens )*; Vishal Bidawatka (The International Institute of Information Technology Hyderabad); Varsha Raveendran (Siemens Technology and Services); Vinay Sudhakaran (Siemens)
• "Modular Visual Navigation using Active Neural Mapping" by Devendra Singh Chaplot (Carnegie Mellon University)*; Saurabh Gupta (Facebook AI Research & UIUC); Abhinav Gupta (CMU/FAIR); Ruslan Salakhutdinov (Carnegie Mellon University)
• "Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning" by Michael Zhang (Harvard University)*
• "Hallucinative Topological Memory for Visual Robotic Manipulation" by Kara Liu (UC Berkeley)*
• "Tetris Battle – A New Environment for Single mode and Double Mode Game" by Yi-Lin Sung (National Taiwan University)*
• "Playing Dota 2 with Large Scale Deep Reinforcement Learning" by OpenAI, Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyłsaw Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang
• "Automated Reverse Curriculum generation from Demonstrations" by Anirudh Srinivasan (Microsoft Research)*; Dzmitry Bahdanau (University of Montreal); Maxime Chevalier-Boisvert (Mila); Yoshua Bengio (Mila)
• "Be a Copycat: Uncharted Rewards by Mimicking Expert Action Sequences" by Tharun Medini (Rice University)*; Anshumali Shrivastava (Rice University)
• "Learning Latent State Spaces for Planning through Reward Prediction" by Aaron J Havens (University of Illinois Urbana Champaign)*; Yi Ouyang (Preferred Networks); Prabhat Nagarajan (Preferred Networks); Yasuhiro Fujita (Preferred Networks, Inc.)
• "Discrete Representations for Goal-Conditioned Reinforcement Learning" by Michael Laskin (UC Berkeley)*
• "CSG Tree LSTM: Parsing CSG Image with Entropy Regulated REINFORCE" by Chenghui Zhou (Carnegie Mellon University)*; Chun-Liang Li (Carnegie Mellon University)
• "AVID: Translating Human Demonstrations for Automated Training" by Laura M Smith (UC Berkeley)*; Nikita Dhawan (UC Berkeley); Marvin Zhang (UC Berkeley); Pieter Abbeel (UC Berkeley); Sergey Levine (UC Berkeley)
• "Improving Evolutionary Strategies With Past Descent Directions" by Asier Mujika (ETH Zurich)*; Florian Meier (ETH Zurich); Marcelo Matheus Gauy (ETH Zurich); Angelika Steger (ETH Zurich)
• "SEERL : Sample Efficient Ensemble Reinforcement Learning" by Rohan Saphal (Indian Institute of Technology Madras)*; Balaraman Ravindran (Indian Institute of Technology, Madras); Dheevatsa Mudigere (Facebook); Sasikanth Avancha (); Bharat Kaul (Intel Labs)
• "On the Design of Variational RL Algorithms" by Joe Marino (California Institute of Technology)*; Alexandre Piché (Université de Montréal); Christopher Pal (École Polytechnique de Montréal ); Yisong Yue (Caltech)
• "ChainerRL: A Deep Reinforcement Learning Library" by Yasuhiro Fujita (Preferred Networks, Inc.)*; Toshiki Kataoka (Preferred Networks, Inc.); Prabhat Nagarajan (Preferred Networks); Takahiro Ishikawa (The University of Tokyo)
• "Benchmarking Safe Exploration in Deep Reinforcement Learning" by Alex Ray (OpenAI)*; Joshua Achiam (OpenAI)
• "Towards Characterizing Divergence in Deep Q-Learning" by Joshua Achiam (OpenAI)*; Ethan Knight (OpenAI); Pieter Abbeel (UC Berkeley)
• "Leveraging Large-Scale Robot Manipulation Datas for Control with Selective Offline Imitation Learning" by Ajay Mandlekar (Stanford University)*; Animesh Garg (Stanford, Nvidia); Fabio Ramos (NVIDIA, The University of Sydney)
• "A Study of Regularization in Policy Optimization" by Zhuang Liu (UC Berkeley)*; Xuanlin Li (UC Berkeley); Bingyi Kang (National University of Singapore); Trevor Darrell (UC Berkeley)
• "Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards" by Xingyu Lu (Berkeley)*; Stas Tiomkin (BAIR, UC Berkeley); Pieter Abbeel (UC Berkeley)
• "Confidential Policies: Preventing Imitation Learning through Context-Adversarial Policy Ensembles" by Albert Zhan (Berkeley)*
• "Efficient Visual Control by Latent Imagination" by Danijar Hafner (Google)*; Timothy Lillicrap (DeepMind); Jimmy Ba (University of Toronto); Mohammad Norouzi (Google Brain)
• "Corpus Compression for Deep Reinforcement Learning in Natural Language Environments" by Zhiwen Tang (Georgetown University)*; Grace Hui Yang (Georgetown University)
• "Intrinsic Motivation in Entropic Environments Through Surprise Minimization" by Glen Berseth (University of California Berkeley)*; Coline Devin (UC Berkeley); Daniel Geng (UC Berkeley); Dinesh Jayaraman (UC Berkeley); Chelsea Finn (UC Berkeley); Sergey Levine (UC Berkeley)
• "Learning efficient representation for intrinsic motivation" by Ruihan Zhao (University of California, Berkeley)*; Stas Tiomkin (BAIR, UC Berkeley); Pieter Abbeel (UC Berkeley)
• "Adaptive Online Planning for Lifelong Reinforcement Learning" by Kevin Lu (UC Berkeley)*; Igor Mordatch (); Pieter Abbeel (UC Berkeley)
• "Thinking While Moving: Deep Reinforcement Learning in Non-Blocking Environments" by Ted Xiao (Google)*; Eric Jang (Google Brain); Dmitry Kalashnikov (Google Inc.); Sergey Levine (Google); Julian Ibarz (Google); Karol Hausman (Google); Alexander Herzog ([X])
• "Learning Theory of Mind for Deep Reinforcement Learning" by Michael W Walton (NIWC)*; Andrew Fuchs (NIWC); Theresa Chadwick (NIWC); Doug Lange (Naval Information Warfare Center Pacific)
• "Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems" by Jeff Druce (Charles River Analytics)*; James Tittle (Charles River Analytics); Michael R Harradon (Charles River Analytics)
• "DoorGym: A Scalable Door Opening Environment and Baseline Agent" by Yusuke Urakami (Panasonic Beta)*; Alec Hodgkinson (Panasonic Beta); Casey Carlin (Panasonic Beta); Randall Leu (Panasonic Beta); Luca Rigazio (Panasonic); Pieter Abbeel (UC Berkeley)
• "Meta-learning curiosity algorithms" by Ferran Alet (MIT)*; Martin Schneider (MIT); Tomas Lozano-Perez (MIT); Leslie Kaelbling (MIT)
• "Swarm-inspired Reinforcement Learning via Collaborative Inter-agent Knowledge Distillation" by Zhang-Wei Hong (Preferred Networks)*; Prabhat Nagarajan (Preferred Networks); Guilherme Maeda (Preferred Networks)
• "Measuring the Reliability ofReinforcement Learning Algorithms" by Stephanie CY Chan (Google)*
• "Deep Dynamics Models for Dexterous Manipulation" by Anusha Nagabandi (UC Berkeley)*
• "Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization" by Hao Liu (UC Berkeley)*; Richard Socher (Salesforce); Caiming Xiong (Salesforce Research)
• "Multi-Task Reinforcement Learning without Interference" by Tianhe Yu (Stanford University)*; Saurabh Kumar (Google); Abhishek Gupta (UC Berkeley); Karol Hausman (Google Brain); Sergey Levine (UC Berkeley); Chelsea Finn (UC Berkeley)
• "Learning with Identity and Uniqueness through Social Constraint" by Hao Sun (CUHK)*; Jiankai Sun (SenseTime Group Limited); Zhenghao Peng (The Chinese University of Hong Kong); Dahua Lin (The Chinese University of Hong Kong); Bolei Zhou (CUHK)
• "Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions" by Robert T Lange (Imperial College London)*; Aldo Faisal (Imperial College London)
• "Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing" by Ge Liu (MIT CSAIL)*; Heng-Tze Cheng (Google Research); Rui Wu (Google Research); Jing Wang (Google Research); Jayiden Ooi (Google); Lihong Li (Google Brain); Ang Li (DeepMind, Mountain View); Sibon Li (DeepMind,Mountain View); Craig Boutilier (Google Research)
• "Risk-Averse Domain Adaptation Under Uncertain Dynamics" by Dinesh Jayaraman (UC Berkeley)*; Jesse Zhang (UC Berkeley); Brian Cheung (UC Berkeley); Chelsea Finn (UC Berkeley); Sergey Levine (UC Berkeley)
• "Ecological Reinforcement Learning" by John D Co-Reyes (UC Berkeley)*; Suvansh Q Sanjeev (UC Berkeley); Glen Berseth (University of California Berkeley); Abhishek Gupta (UC Berkeley); Sergey Levine (UC Berkeley)
• "Sparse Skill Coding: Learning Behavioral Hierarchies with Efficient Coding" by Sophia Sanborn (UC Berkeley)*; Michael Chang (University of California, Berkeley); Sergey Levine (UC Berkeley); Thomas Griffiths (Princeton University)

Poster Session 1: 15:00 - 16:00

• TBA

Poster Session 2: 18:00 - 19:00

• TBA