OverviewPlanning and learning are both core areas of Artificial Intelligence. The reinforcement learning community has mostly relied on approximate dynamic programming and Monte-Carlo tree search as its workhorses for planning, while the field of planning has developed a diverse set of representational formalisms and scalable algorithms that are currently underexplored in learning approaches. Further, the planning community could benefit from the tools and algorithms developed by the machine learning community, for instance to automate the generation of planning problem descriptions.
The purpose of this workshop is to encourage discussion and collaboration between the communities of planning and learning. Furthermore, we also expect that agents and general AI researchers are interested in the intersection of planning and learning, in particular those that focus on intelligent decision making. As such, the joint workshop program is an excellent opportunity to gather a large and diverse group of interested researchers. Organizing CommitteeInvited SpeakersThore Graepel, Google DeepMind
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)
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)The Planning and Learning workshop solicits work at the intersection of the fields of machine learning and planning. We also solicit work solely in one area that can influence advances in the other so long as the connections are clearly articulated in the submission. Submissions are invited for topics on, but not limited to: - Multi-agent planning and learning
- Robust planning in uncertain (learned) models
- Adaptive Monte Carlo planning
- Learning search heuristics for planner guidance
- Reinforcement learning (model-based, Bayesian, deep, etc.)
- Model representation and learning for planning
- Theoretical aspects of planning and learning
- Learning and planning competition(s)
- Applications of planning and learning
Important DatesSubmission deadline: May 23, 2018 (11:59pm Hawaii Time)Notification date: May 31, 2018Camera-ready deadline: Wednesday, June 13, 2018- Workshop date: Sunday, July 15, 2018 (full day)
Submission Procedure (closed)We solicit workshop paper submissions relevant to the above call of the following types: - Long papers -- up to 8 pages + unlimited references / appendices
- Short papers -- up to 4 pages + unlimited references / appendices
- Extended abstracts -- up to 2 pages + unlimited references / appendices
We will accept papers in any of the IJCAI, ICML, AAMAS, or NIPS formats. Submissions are not anonymous and should include author information.
Some accepted papers will be accepted as contributed talks. All other papers will be given a slot in the poster presentation session. Extended abstracts are intended as brief summaries of already published papers, challenge or position papers, or preliminary work.
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