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Tobias Meisen* -* [talk] **Online Planning in MDPs with Stochastic Durative Actions** *Tal Berman, Ronen Brafman, Erez Karpas* -* [talk] **ModelDiff: Leveraging Models for Policy Transfer with Value Lower Bounds** *Xiaotian Liu, Jihwan Jeong, Ayal Taitler, Michael Gimelfarb, Scott Sanner* -* [poster] **Solving Minecraft Tasks via Model Learning** *Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern* -* [talk] **A New View on Planning in Online Reinforcement Learning** *Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White* -* [poster] **Conviction-Based Planning for Sparse Reward Reinforcement Learning Problems** *Simon Ouellette, Eric Beaudry, Mohamed Bouguessa* -* [poster] **Q\* Search: Heuristic Search with Deep Q-Networks** *Forest Agostinelli, Shahaf S. Shperberg, Alexander Shmakov, Stephen Marcus McAleer, Roy Fox, Pierre Baldi* -* [poster] **Finding Reaction Mechanism Pathways with Deep Reinforcement Learning and Heuristic Search** *Rojina Panta, Mohammadamin Tavakoli, Christian Geils, Pierre Baldi, Forest Agostinelli* -* [talk] **Planning with Language Models Through The Lens of Efficiency** *Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi* -* [poster] **Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning** *Brandon Rozek, Junkyu Lee, Harsha Kokel, Michael Katz, Shirin Sohrabi* -* [poster] **Monte Carlo Tree Search for Integrated Planning, Learning, and Execution in Nondeterministic Python** *Rich Levinson* -* [talk] **Exploring Simultaneity: Learning Earliest-time Semantics for Automated Planning** *Ángel Aso-Mollar, Óscar Sapena, Eva Onaindia* -* [poster] **Numeric Reward Machines** *Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis, Aneta Vulgarakis Feljan, Jendrik Seipp* -* [poster] **POSGGym: A Library for Decision-Theoretic Planning and Learning in Partially Observable, Multi-Agent Environments** *Jonathon Schwartz, Rhys Newbury, Dana Kulic, Hanna Kurniawati* -* [talk] **The Case for Developing a Foundation Model for Planning-like Tasks from Scratch** *Biplav Srivastava, Vishal Pallagani* -* [talk] **Equivalence-Based Abstractions for Learning General Policies** *Dominik Drexler, Simon Ståhlberg, Blai Bonet, Hector Geffner* -* [talk] **Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning** *Katharina Stein, Daniel Fišer, Jörg Hoffmann, Alexander Koller* -* [talk] **Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning** *Nicola J. Müller, Pablo Sanchez Martin, Jörg Hoffmann, Verena Wolf, Timo P. Gros* -* [poster] **Towards Neurosymbolic RL via Inductive Learning of Answer Set Programs** *Celeste Veronese, Daniele Meli, Alessandro Farinelli* -* [poster] **SLOPE: Search with Learned Optimal Pruning-based Expansion** *Davor Bokan, Zlatan Ajanović, Bakir Lacevic* +* [poster] [**Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning**](prl-papers/2.pdf) *Guy Azran, Mohamad Hosein Danesh, Stefano V Albrecht, Sarah Keren* +* [talk] [**Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling**](prl-papers/3.pdf) *Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters, Hasan Tercan, Tobias Meisen* +* [talk] [**Online Planning in MDPs with Stochastic Durative Actions**](prl-papers/4.pdf) *Tal Berman, Ronen Brafman, Erez Karpas* +* [talk] [**ModelDiff: Leveraging Models for Policy Transfer with Value Lower Bounds**](prl-papers/5.pdf) *Xiaotian Liu, Jihwan Jeong, Ayal Taitler, Michael Gimelfarb, Scott Sanner* +* [poster] [**Solving Minecraft Tasks via Model Learning**](prl-papers/6.pdf) *Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern* +* [talk] [**A New View on Planning in Online Reinforcement Learning**](prl-papers/7.pdf) *Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White* +* [poster] [**Conviction-Based Planning for Sparse Reward Reinforcement Learning Problems**](prl-papers/8.pdf) *Simon Ouellette, Eric Beaudry, Mohamed Bouguessa* +* [poster] [**Q\* Search: Heuristic Search with Deep Q-Networks**](prl-papers/9.pdf) *Forest Agostinelli, Shahaf S. Shperberg, Alexander Shmakov, Stephen Marcus McAleer, Roy Fox, Pierre Baldi* +* [poster] [**Finding Reaction Mechanism Pathways with Deep Reinforcement Learning and Heuristic Search**](prl-papers/10.pdf) *Rojina Panta, Mohammadamin Tavakoli, Christian Geils, Pierre Baldi, Forest Agostinelli* +* [talk] [**Planning with Language Models Through The Lens of Efficiency**](prl-papers/11.pdf) *Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi* +* [poster] [**Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning**](prl-papers/12.pdf) *Brandon Rozek, Junkyu Lee, Harsha Kokel, Michael Katz, Shirin Sohrabi* +* [poster] [**Monte Carlo Tree Search for Integrated Planning, Learning, and Execution in Nondeterministic Python**](prl-papers/13.pdf) *Rich Levinson* +* [talk] [**Exploring Simultaneity: Learning Earliest-time Semantics for Automated Planning**](prl-papers/14.pdf) *Ángel Aso-Mollar, Óscar Sapena, Eva Onaindia* +* [poster] [**Numeric Reward Machines**](prl-papers/16.pdf) *Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis, Aneta Vulgarakis Feljan, Jendrik Seipp* +* [poster] [**POSGGym: A Library for Decision-Theoretic Planning and Learning in Partially Observable, Multi-Agent Environments**](prl-papers/17.pdf) *Jonathon Schwartz, Rhys Newbury, Dana Kulic, Hanna Kurniawati* +* [talk] [**The Case for Developing a Foundation Model for Planning-like Tasks from Scratch**](prl-papers/18.pdf) *Biplav Srivastava, Vishal Pallagani* +* [talk] [**Equivalence-Based Abstractions for Learning General Policies**](prl-papers/19.pdf) *Dominik Drexler, Simon Ståhlberg, Blai Bonet, Hector Geffner* +* [talk] [**Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning**](prl-papers/20.pdf) *Katharina Stein, Daniel Fišer, Jörg Hoffmann, Alexander Koller* +* [talk] [**Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning**](prl-papers/23.pdf) *Nicola J. Müller, Pablo Sanchez Martin, Jörg Hoffmann, Verena Wolf, Timo P. Gros* +* [poster] [**Towards Neurosymbolic RL via Inductive Learning of Answer Set Programs**](prl-papers/24.pdf) *Celeste Veronese, Daniele Meli, Alessandro Farinelli* +* [poster] [**SLOPE: Search with Learned Optimal Pruning-based Expansion**](prl-papers/25.pdf) *Davor Bokan, Zlatan Ajanović, Bakir Lacevic* ## Submission Details