NeurIPS 2019 Deep RL Workshop

Hear directly from presenters at the NeurIPS 2019 Deep RL Workshop on their work!

Show Notes

Thank you to all the presenters that participated.  I covered as many as I could given the time and crowds, if you were not included and wish to be, please email [e-mail] 

More details on the official NeurIPS Deep RL Workshop site. 

• 0:23  Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms; Matthia Sabatelli (University of Liege); Gilles Louppe (University of Liège); Pierre Geurts (University of Liège); Marco Wiering (University of Groningen) [external pdf link] 
• 4:16  Single Deep Counterfactual Regret Minimization; Eric Steinberger (University of Cambridge). 
• 5:38  On the Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER; 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). 
• 9:33  Objective Mismatch in Model-based Reinforcement Learning; Nathan Lambert (UC Berkeley); Brandon Amos (Facebook); Omry Yadan (Facebook); Roberto Calandra (Facebook). 
• 10:51  Option Discovery using Deep Skill Chaining; Akhil Bagaria (Brown University); George Konidaris (Brown University). 
• 13:44  Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware; Kirill Polzounov (University of Calgary); Ramitha Sundar (Blue River Technology); Lee Reden (Blue River Technology). 
• 14:52  LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games; Leonard Adolphs (ETHZ); Thomas Hofmann (ETH Zurich). 
• 16:30  Accelerating Training in Pommerman with Imitation and Reinforcement Learning; Hardik Meisheri (TCS Research); Omkar Shelke (TCS Research); Richa Verma (TCS Research); Harshad Khadilkar (TCS Research). 
• 17:27  Dream to Control: Learning Behaviors by Latent Imagination; Danijar Hafner (Google); Timothy Lillicrap (DeepMind); Jimmy Ba (University of Toronto); Mohammad Norouzi (Google Brain) [external pdf link]. 
• 20:48  Adaptive Temperature Tuning for Mellowmax in Deep Reinforcement Learning; Seungchan Kim (Brown University); George Konidaris (Brown). 
• 22:05  Meta-learning curiosity algorithms; Ferran Alet (MIT); Martin Schneider (MIT); Tomas Lozano-Perez (MIT); Leslie Kaelbling (MIT). 
• 24:09  Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards; Xingyu Lu (Berkeley); Stas Tiomkin (BAIR, UC Berkeley); Pieter Abbeel (UC Berkeley). 
• 25:44   Swarm-inspired Reinforcement Learning via Collaborative Inter-agent Knowledge Distillation; Zhang-Wei Hong (Preferred Networks); Prabhat Nagarajan (Preferred Networks); Guilherme Maeda (Preferred Networks). 
• 26:35  Multiplayer AlphaZero; Nicholas Petosa (Georgia Institute of Technology); Tucker Balch (Ga Tech) [external pdf link]. 
• 27:43  Prioritized Sequence Experience Replay; Marc Brittain (Iowa State University); Joshua Bertram (Iowa State University); Xuxi Yang (Iowa State University); Peng Wei (Iowa State University) [external pdf link]. 
• 29:14  Recurrent neural-linear posterior sampling for non-stationary bandits; Paulo Rauber (IDSIA); Aditya Ramesh (USI); Jürgen Schmidhuber (IDSIA - Lugano). 
• 29:36  Improving Evolutionary Strategies With Past Descent Directions; Asier Mujika (ETH Zurich); Florian Meier (ETH Zurich); Marcelo Matheus Gauy (ETH Zurich); Angelika Steger (ETH Zurich) [external pdf link]. 
• 31:40  ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations; Daniel Seita (University of California, Berkeley); David Chan (University of California, Berkeley); Roshan Rao (UC Berkeley); Chen Tang (UC Berkeley); Mandi Zhao (UC Berkeley); John Canny (UC Berkeley) [external pdf link]. 
• 33:05  Bottom-Up Meta-Policy Search; Luckeciano Melo (Aeronautics Institute of Technology); Marcos Máximo (Aeronautics Institute of Technology); Adilson Cunha (Aeronautics Institute of Technology) [external pdf link]. 
• 33:37  MERL: Multi-Head Reinforcement Learning; Yannis Flet-Berliac (University of Lille / Inria); Philippe Preux (INRIA) [external pdf link]. 
• 35:30  Emergent Tool Use from Multi-Agent Autocurricula; Bowen Baker (OpenAI); Ingmar Kanitscheider (OpenAI); Todor Markov (OpenAI); Yi Wu (UC Berkeley); Glenn Powell (OpenAI); Bob McGrew (OpenAI); Igor Mordatch (). 
• 37:09  Learning an off-policy predictive state representation for deep reinforcement learning for vision-based steering in autonomous driving; Daniel Graves (Huawei) 
• 39:37  Multi-Task Reinforcement Learning without Interference; Tianhe Yu (Stanford University); Saurabh Kumar (Stanford); Abhishek Gupta (UC Berkeley); Karol Hausman (Google Brain); Sergey Levine (UC Berkeley); Chelsea Finn (UC Berkeley). 
• 40:52  Behavior-Regularized Offline Reinforcement Learning; Yifan Wu (Carnegie Mellon University); George Tucker (Google Brain); Ofir Nachum (Google) [external pdf link]. 
• 42:36  If MaxEnt RL is the Answer, What is the Question?; Ben Eysenbach (Carnegie Mellon University); Sergey Levine (UC Berkeley) [external pdf link]. 
• 43:30  Receiving Uncertainty-Aware Advice in Deep Reinforcement Learning; Felipe Leno da Silva (University of Sao Paulo); Pablo Hernandez-Leal (Borealis AI); Bilal Kartal (Borealis AI); Matthew Taylor (Borealis AI). 
• 45:03  Striving for Simplicity in Off-Policy Deep Reinforcement Learning; Rishabh Agarwal (Google Research, Brain Team); Dale Schuurmans (Google / University of Alberta); Mohammad Norouzi (Google Brain) [external pdf link]. 
• 45:32  Interactive Fiction Games: A Colossal Adventure; Matthew Hausknecht (Microsoft Research); Prithviraj Ammanabrolu (Georgia Institute of Technology); Marc-Alexandre Côté (Microsoft Research); Xingdi Yuan (Microsoft Research) [external pdf link]. 
• 52:20  rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch; Adam Stooke (UC Berkeley); Pieter Abbeel (UC Berkeley)  [ Repo: https://github.com/astooke/rlpyt ] 
• 53:49  Learning to Drive using Waypoints; Tanmay Agarwal, Hitesh Arora, Tanvir Parhar, Shubhankar V Deshpande, Jeff Schneider - from the NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving Workshop 
  
  

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