Timezone: »
Reinforcement Learning (RL) has had numerous successes in recent years in solving complex problem domains. However, this progress has been largely limited to domains where a simulator is available or the real environment is quick and easy to access. This is one of a number of challenges that are bottlenecks to deploying RL agents on real-world systems. Two recent papers identify nine important challenges that, if solved, will take a big step towards enabling RL agents to be deployed to real-world systems (Dulac et. al. 2019, 2020).The goals of this workshop are four-fold: (1) Providing a forum for researchers in academia, industry researchers as well as industry practitioners from diverse backgrounds to discuss the challenges faced in real-world systems; (2) discuss and prioritize the nine research challenges. This includes determining which challenges we should focus on next, whether any new challenges should be added to the list or existing ones removed from this list; (3) Discuss problem formulations for the various challenges and critique these formulations or develop new ones. This is especially important for more abstract challenges such as explainability. We should also be asking ourselves whether the current Markov Decision Process (MDP) formulation is sufficient for solving these problems or whether modifications need to be made. (4) Discuss approaches to solving combinations of these challenges.
Sat 8:30 a.m. - 8:40 a.m.
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Introduction and Overview (Introduction) | Daniel Mankowitz, Gabe Dulac-Arnold |
Sat 8:40 a.m. - 9:20 a.m.
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Keynote: Aviv Tamar
(Talk)
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Video
Real World RL Challenges |
Aviv Tamar |
Sat 9:20 a.m. - 10:00 a.m.
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Keynote: Emma Brunskill
(Talk)
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Video
More practical Batch Offline Reinforcement Learning |
Emma Brunskill |
Sat 10:00 a.m. - 10:40 a.m.
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Keynote: Jost Tobias Springenberg
(Talk)
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Challenges for RL in Robotics |
Jost Tobias Springenberg |
Sat 10:40 a.m. - 11:20 a.m.
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Mini-panel discussion 1 - Bridging the gap between theory and practice
(Discussion Panel)
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Aviv Tamar, Emma Brunskill, Jost Tobias Springenberg, Omer Gottesman, Daniel Mankowitz |
Sat 11:20 a.m. - 11:50 a.m.
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Poster session 1
(Poster Session and Coffee Break)
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You can now chat to the paper authors by clicking the above Gather.town link Links to individual poster presentations can be found here: https://sites.google.com/corp/view/neurips2020rwrl#h.ey6lwdtrdt7c |
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Sat 11:50 a.m. - 12:30 p.m.
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Keynote: Franziska Meier
(Talk)
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Video
Challenges of Model-based Inverse Reinforcement Learning |
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Sat 12:30 p.m. - 1:10 p.m.
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Keynote: Marc Raibert, Scott Kuindersma
(Talk)
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Video
Boston Dynamics |
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Sat 1:10 p.m. - 1:50 p.m.
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Mini-panel discussion 2 - Real World RL: An industry perspective
(Discussion Panel)
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The following speakers that will be at this event do not have Neurips profiles: Franziska Meier - fmeier@fb.com Marc Reibert - marcraibert@bostondynamics.com Scott Kuindersma - skuindersma@bostondynamics.com |
Franziska Meier, Gabriel Dulac-Arnold, Shie Mannor, Timothy A Mann |
Sat 1:50 p.m. - 3:20 p.m.
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Lunch
(Lunch Break)
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Enjoy your lunch break. If you intend to attend the 3rd mini-panel session, we encourage you to watch the talks of Anca Dragan and Angela Schoellig during lunch as their keynote talks will only occur after the mini-panel session. Thus, if you want to ask them questions, please take the time to watch the talks now. |
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Sat 3:20 p.m. - 4:00 p.m.
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Spotlight Talks
(Talk)
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We have 4 spotlight talks. These talks can be found at the following link: https://sites.google.com/corp/view/neurips2020rwrl#h.9w5kdo7eecim |
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Sat 4:00 p.m. - 4:40 p.m.
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Keynote: Tom Diettrich
(Talk)
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Video
Applying RL to Ecosystem Management: Lessons Learned |
Tom Dietterich |
Sat 4:40 p.m. - 5:20 p.m.
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Keynote: Chelsea Finn
(Talk)
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Video
Reinforcement Learning for Real Robots |
Chelsea Finn |
Sat 5:20 p.m. - 6:00 p.m.
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Mini-panel discussion 3 - Prioritizing Real World RL Challenges
(Discussion Panel)
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Chelsea Finn, Tom Dietterich, Angela Schoellig, Anca Dragan, Anusha Nagabandi, Doina Precup |
Sat 6:00 p.m. - 6:30 p.m.
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Poster session 2
(Poster Session and Coffee Break)
»
You can now chat to the paper authors by clicking the above Gather.town link Links to individual poster presentations can be found here: https://sites.google.com/corp/view/neurips2020rwrl#h.ey6lwdtrdt7c |
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Sat 6:30 p.m. - 7:10 p.m.
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Keynote: Angela Schoellig
(Talk)
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Machine Learning for Safety-Critical Robotics Applications |
Angela Schoellig |
Sat 7:10 p.m. - 7:50 p.m.
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Keynote: Anca Dragan
(Talk)
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Video
Reinforcement Learning that optimizes what people really want |
Anca Dragan |
Author Information
Daniel Mankowitz (DeepMind)
Gabriel Dulac-Arnold (Google Research)
Shie Mannor (Technion)
Omer Gottesman (Harvard)
Anusha Nagabandi (UC Berkeley)
Doina Precup (DeepMind)
Timothy A Mann (DeepMind)
Gabe Dulac-Arnold (Google Research)
More from the Same Authors
-
2020 Poster: Value-driven Hindsight Modelling »
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2020 Poster: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning »
Caglar Gulcehre · Ziyu Wang · Alexander Novikov · Thomas Paine · Sergio Gómez · Konrad Zolna · Rishabh Agarwal · Josh Merel · Daniel Mankowitz · Cosmin Paduraru · Gabriel Dulac-Arnold · Jerry Li · Mohammad Norouzi · Matthew Hoffman · Nicolas Heess · Nando de Freitas -
2020 Poster: On Efficiency in Hierarchical Reinforcement Learning »
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2020 Poster: Online Planning with Lookahead Policies »
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2020 Poster: Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model »
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2020 Spotlight: On Efficiency in Hierarchical Reinforcement Learning »
Zheng Wen · Doina Precup · Morteza Ibrahimi · Andre Barreto · Benjamin Van Roy · Satinder Singh -
2019 Poster: Distributional Policy Optimization: An Alternative Approach for Continuous Control »
Chen Tessler · Guy Tennenholtz · Shie Mannor -
2019 Poster: Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates »
Carlos Riquelme · Hugo Penedones · Damien Vincent · Hartmut Maennel · Sylvain Gelly · Timothy A Mann · Andre Barreto · Gergely Neu -
2019 Demonstration: The Option Keyboard: Combining Skills in Reinforcement Learning »
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Andre Barreto · Diana Borsa · Shaobo Hou · Gheorghe Comanici · Eser Aygün · Philippe Hamel · Daniel Toyama · Jonathan hunt · Shibl Mourad · David Silver · Doina Precup -
2019 Poster: Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning »
Chao Qu · Shie Mannor · Huan Xu · Yuan Qi · Le Song · Junwu Xiong -
2019 Poster: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Spotlight: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2018 Poster: Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning »
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