Schedule

The workshop is preceded by the conference’s first invited talk.


9:45–9:50 Opening remarks
9:50–10:20 Invited talk #1: Pieter Abbeel, “To be announced
10:20–10:30 Contributed talk #1: Kate Rakelly, “Efficient off-policy meta-reinforcement learning via probabilistic context variables
10:30–11:00 Poster session #1 (during the morning coffee break)

11:00–11:30 Invited talk #2: Matt Botvinick, “To be announced
11:30–12:00 Invited talk #3: Katja Hofmann, “To be announced
12:00–12:30 Invited talk #4: Tejas Kulkarni, “To be announced
12:30–1:00 Invited talk #5: Tim Lillicrap, “To be announced

The workshop will recommence after lunch and the conference’s second invited talk.


3:20–3:50 Invited talk #6: Karthik Narasimhan, “To be announced
3:50–4:00 Contributed talk #2: Ben Eysenbach, “State marginal matching w/ mixtures of policies” & “RL w/ unknown reward functions
4:00–4:30 Poster session #2 (during the afternoon coffee break)

4:30–5:00 Invited talk #7: Doina Precup, “To be announced
5:00–5:30 Invited talk #8: Jane Wang, “To be announced
5:30–6:30 Panel discussion

The workshop is followed by the conference’s opening reception and the newcomers’ reception.

Program

Invited Speakers

Pieter Abbeel (UC Berkeley), “To be announced

Pieter Abbeel


Matt Botvinick (DeepMind), “To be announced

Matt Botvinick


Katja Hofmann (Microsoft Research), “To be announced

Katja Hofmann


Tejas Kulkarni (DeepMind), “To be announced

Tejas Kulkarni


Tim Lillicrap (DeepMind), “To be announced

Tim Lillicrap


Karthik Narasimhan (Princeton), “To be announced

Karthik Narasimhan


Doina Precup (McGill / DeepMind), “To be announced

Doina Precup


Jane Wang (DeepMind), “To be announced

Jane Wang

Invited Panellists

In addition to invited speakers Tejas Kulkarni, Tim Lillicrap, Karthik Narasimhan and Jane Wang, we are happy to have the following invited panellists:

To be announced

Contributed Talks

Kate Rakelly (UC Berkeley), “Efficient off-policy meta-reinforcement learning via probabilistic context variables

Kate Rakelly


Ben Eysenbach (Carnegie Mellon), “State marginal matching with mixtures of policies” & “Reinforcement learning with unknown reward functions

Ben Eysenbach

Contributed Posters

Poster Session #1

(39) Efficient off-policy meta-reinforcement learning via probabilistic context variables Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine

(1) Feudal multi-agent hierarchies for cooperative reinforcement learning Sanjeevan Ahilan, Peter Dayan

(6) Few-shot imitation learning with disjunctions of conjunctions of programs Tom Silver, Kelsey Allen, Leslie Kaelbling, Joshua Tenenbaum

(7) Graph-DQN: Fast generalization to novel objects using prior relational knowledge Varun Kumar, Hanlin Tang, Arjun K Bansal

(15) Learning powerful policies by using consistent dynamics model Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

(11) Learning to generalize from sparse and underspecified rewards Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

(40) Meta-reinforcement learning with autonomous task inference Sungryull Sohn, Honglak Lee

(44) Meta-learning surrogate models for sequential decision making Jonathan Schwarz, Alexandre Galashov, Yee Whye Teh, Marta Garnelo, David Saxton, S. M. Ali Eslami, Pushmeet Kohli, Hyunjik Kim

(35) Mimicry constraint policy optimization Xiaojian Ma, Mingxuan Jing, Fuchun Sun, Huaping Liu

(29) Perception-prediction-reaction agents for deep reinforcement learning Adam Stooke, Max Jaderberg, Valentin Dalibard, Siddhant Jayakumar, Wojciech M. Czarnecki

(19) Rapid trial-and-error learning in physical problem solving Kelsey Allen, Kevin Smith, Joshua Tenenbaum

(21) Recurrent learning reinforcement learning Pierre Thodoroff, Nishanth V. Anand, Lucas Caccia, Doina Precup, Joelle Pineau

(27) Search on the replay buffer: Bridging motion planning and reinforcement learning Ben Eysenbach, Sergey Levine, Ruslan Salakhutdinov

(32) Skill discovery with well-defined objectives Yuu Jinnai, David Abel, Jee Won Park, David Hershkowitz, Michael L. Littman, George Konidaris

(23) Structured mechanical models for efficient reinforcement learning Kunal R Menda, Jayesh K Gupta, Zachary Manchester, Mykel Kochenderfer

(30) Variational task embeddings for fast adaptation in deep reinforcement learning Luisa M. Zintgraf, Kyriacos Shiarli, Maximilian Igl, Anuj Mahajan, Katja Hofmann, Shimon Whiteson

Poster Session #2

(16) Bayesian policy selction using active inference Ozan Catal

(10) Control what you can: Intrinsically motivated reinforcement learner with task planning structure Sebastian Blaes, Marin Vlastelica, Jia-Jie Zhu, Georg Martius

(18) Decoupling feature extraction from policy learning: Assessing benefits of state representation learning in goal based robotics Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Dı́az Rodrı́guez, David Filliat

(34) Exploiting hierarchy for learning and transfer in KL-regularized RL Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess

(12) Language as an abstraction for hierarchical deep reinforcement learning YiDing Jiang, Chelsea Finn, Shixiang Gu, Kevin Murphy

(22) Learning effect-dependent embeddings for temporal abstraction William Whitney, Abhinav Gupta

(43) Perception-aware point-based value iteration for partially observable markov decision processes Mahsa Ghasemi, Ufuk Topcu

(38) Planning with latent simulated trajectories Alexandre Piché, Valentin Thomas, Cyril Ibrahim, Julien Cornebise, Chris Pal

(41) Proprioceptive spatial representations for generalized locomotion Joshua Zhanson, Emilio Parisotto, Ruslan Salakhutdinov

(9) Provably efficient RL with rich observations via latent state decoding Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford

(26) Reinforcement learning with unknown reward functions Ben Eysenbach, Jacob Tyo, Shixiang Gu, Ruslan Salakhutdinov, Zachary Lipton, Sergey Levine

(25) State marginal matching with mixtures of policies Ben Eysenbach, Sergey Levine, Lisa Lee, Emilio Parisotto, Ruslan Salakhutdinov

(17) Symmetry-based disentangled representation learning requires interaction with environments Hugo Caselles-Dupré, David Filliat, Michael Garcia Ortiz

(14) Task-agnostic dynamics priors for deep reinforcement learning Yilun Du, Karthik Narasimhan

(37) Unsupervised subgoal discovery method for learning hierarchical representations Jacob Rafati, David C. Noelle

(20) Value preserving state-action abstractions David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman