NeurIPS 2019 Deep RL Workshop
Hear directly from presenters at the NeurIPS 2019 Deep RL Workshop on their work!
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 talkrl@pathwayi.com
More details on the official NeurIPS Deep RL Workshop site.
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
Creators and Guests
Host
Robin Ranjit Singh Chauhan
🌱 Head of Eng @AgFunder 🧠 AI:Reinforcement Learning/ML/DL/NLP🎙️Host @TalkRLPodcast 💳 ex-@Microsoft ecomm PgmMgr 🤖 @UWaterloo CompEng 🇨🇦 🇮🇳
