Overview

Organizing Committee

Scott Sanner, University of Toronto

Matthijs Spaan, TU Delft

Timothy Mann, Google DeepMind

Aviv Tamar, UC Berkeley

Invited Speakers

Schedule (Sunday, July 15, Room C7)

8:30-10:00: Morning Session 1

Invited Talk (8:30-9:15): Emma Brunskill, Stanford University, "Planning to Learn"

Contributed Paper Talks (9:15-10:00) "Safety and Robustness":

- "Safe Reduced Models for Probabilistic Planning", Sandhya Saisubramanian and Shlomo Zilberstein.

- "An Empirical Evaluation of Safe Policy Improvement in Factored Environments", Thiago D. Simão and Matthijs T. J. Spaan.

- "Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes", Andrea Tirinzoni, Xiangli Chen, Marek Petrik and Brian Ziebart.

10:30-12:45: Morning Session 2

Invited Talk (10:30-11:15): Craig Boutilier, Google Mountain View, "RL and MDPs in Recommender Systems: Modeling and Computational Challenges"

Contributed Paper Talks (11:15-12:15) "Learning and Planning I":

- "Planning to Give Information in Partially Observed Domains with a Learned Weighted Entropy Model", Rohan Chitnis, Leslie Kaelbling and Tomás Lozano-Pérez.

- "Learning to Plan with Portable Symbols", Steven James, Benjamin Rosman and George Konidaris.

- "Learning Plannable Representations with Causal InfoGAN", Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell and Pieter Abbeel.

- "Improving width-based planning with compact policies", Miquel Junyent, Anders Jonsson and Vicenç Gómez.

1st Poster Session (12:15-12:45): All Papers (posters: 61 cm x 91 cm in portrait orientation)

12:45-14:00: Lunch

14:00-15:30: Afternoon Session 1

Invited Talk (14:00-14:45): Sergey Levine, U.C. Berkeley, "Off-Policy Learning with Model-Based and Model-Free RL"

Contributed Paper Talks (14:45-15:30) "Learning and Planning II":

- "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models", Kurtland Chua, Roberto Calandra, Rowan McAllister and Sergey Levine.

- "Recognizing Plans by Learning Embeddings from Observed Action Distributions", Yantian Zha, Yikang Li, Sriram Gopalakrishnan, Baoxin Li and Subbarao Kambhampati.

- "Extracting Action Sequences from Texts Based on Deep Reinforcement Learning", Feng Wenfeng, Hankz Hankui Zhuo and Subbarao Kambhampati.

16:00-18:00: Afternoon Session 2

Invited Talk (16:00-16:45): Thore Graepel, Google DeepMind, "The Role of Multi-Agent Learning in Artificial Intelligence Research"

Contributed Paper Talks (16:45-17:30) "MCTS methods, Constrained (PO)MDPs":

- "A0C: Alpha Zero in Continuous Action Space", Thomas Moerland, Joost Broekens, Aske Plaat and Catholijn Jonker.

- "Monte-Carlo Tree Search for Constrained MDPs", Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart and Kee-Eung Kim.

- "Column Generation Algorithms for Constrained POMDPs", Erwin Walraven and Matthijs T. J. Spaan.

2nd Poster Session (17:30-18:00): All Papers (posters: 61 cm x 91 cm in portrait orientation)

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Accepted Papers

• "Safe Reduced Models for Probabilistic Planning", Sandhya Saisubramanian and Shlomo Zilberstein.
• "Sampling in Routing Problems with Environment Shifts", Keisuke Otaki, Tomoki Nishi and Takayoshi Yoshimura.
• "Planning to Give Information in Partially Observed Domains with a Learned Weighted Entropy Model", Rohan Chitnis, Leslie Kaelbling and Tomás Lozano-Pérez.
• "Hierarchical Reinforcement Learning with Abductive Planning", Kazeto Yamamoto, Takashi Onishi and Yoshimasa Tsuruoka.
• "Conditional plans synthesis for decision u.nder uncertainty applied to satellite acquisitions", Sébastien Piedade, Charles Lesire and Guillaume Infantes.
• "Monte-Carlo Tree Search for Constrained MDPs", Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart and Kee-Eung Kim.
• "Learning Plannable Representations with Causal InfoGAN", Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell and Pieter Abbeel.
• "Direct Model Predictive Control", Shane Barratt.
• "Efficient Control via Exact Equation Learning", Jia-Jie Zhu and Georg Martius.
• "Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes", Andrea Tirinzoni, Xiangli Chen, Marek Petrik and Brian Ziebart.
• "Gradient-Based Search Tree for Continuous Control in Online POMDP Learning", Luchen Li and Aldo A. Faisal.
• "Capacity-aware Sequential Recommendations", Frits de Nijs, Georgios Theocharous, Nikos Vlassis, Mathijs De Weerdt and Matthijs T. J. Spaan.
• "Column Generation Algorithms for Constrained POMDPs", Erwin Walraven and Matthijs T. J. Spaan.
• "Recognizing Plans by Learning Embeddings from Observed Action Distributions", Yantian Zha, Yikang Li, Sriram Gopalakrishnan, Baoxin Li and Subbarao Kambhampati.
• "Monte Carlo Tree Search for Asymmetric Trees", Thomas Moerland, Joost Broekens, Aske Plaat and Catholijn Jonker.
• "Online Optimisation for Online Learning and Control – From No-Regret to Generalised Error Convergence", Jan-Peter Calliess.
• "A0C: Alpha Zero in Continuous Action Space", Thomas Moerland, Joost Broekens, Aske Plaat and Catholijn Jonker.
• "Constrained shortest path search with graph convolutional neural networks", Kevin Osanlou, Christophe Guettier, Andrei Bursuc, Tristan Cazenave and Eric Jacopin.
• "Efficient Resource Allocation in Electricity Management", Oliver Pfante, Niklas Goby, Stefan Smolarek and Dirk Neumann.
• "Learning to Plan with Portable Symbols", Steven James, Benjamin Rosman and George Konidaris.
• "Extracting Action Sequences from Texts Based on Deep Reinforcement Learning", Feng Wenfeng, Hankz Hankui Zhuo and Subbarao Kambhampati.
• "Improving width-based planning with compact policies", Miquel Junyent, Anders Jonsson and Vicenç Gómez.
• "LSTM-Based Goal Recognition in Latent Space", Leonardo Rosa Amado, João Paulo Aires, Ramon Pereira, Maurício Magnaguagno, Roger Granada and Felipe Meneguzzi.
• "A policy gradient algorithm to compute boundedly rational stationary mean field equilibria", Jayakumar Subramanian and Aditya Mahajan.
• "Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks", Seydou Ba, Takuya Hiraoka, Takashi Onishi, Toru Nakata and Yoshimasa Tsuruoka.
• "Safe learning-based optimal motion planning for automated driving", Zlatan Ajanovic, Bakir Lacevic, Georg Stettinger, Daniel Watzenig and Martin Horn.
• "Decision Theoretic Representation and Learning With Large Agent Population", Nguyen Duc Thien, Akshat Kumar and Hoong Chuin Lau.
• "Freeing the Exponents - Schemes for Estimating Generalized Value Functions", Dotan Di Castro.
• "An Empirical Evaluation of Safe Policy Improvement in Factored Environments", Thiago D. Simão and Matthijs T. J. Spaan.
• "Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering", Aleksandr Panov and Aleksey Skrynnik.
• "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models", Kurtland Chua, Roberto Calandra, Rowan McAllister and Sergey Levine.

Call for Submissions (closed)