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Workshop

Generalization in Planning (GenPlan '23)

Pulkit Verma ⋅ Siddharth Srivastava ⋅ Aviv Tamar ⋅ Felipe Trevizan
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Abstract

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Schedule

Timezone: America/Los_Angeles
6:15 AM
Opening Remarks
6:55 AM
Learning Abstract World Models for Value-preserving Planning with Options
Rafael Rodriguez Sanchez ⋅ George Konidaris
7:15 AM
Explore to Generalize in Zero-Shot RL
Ev Zisselman ⋅ Itai Lavie ⋅ Daniel Soudry ⋅ Aviv Tamar
8:00 AM
Coffee Break
9:05 AM
GOOSE: Learning Domain-Independent Heuristics
Dillon Chen ⋅ Felipe Trevizan ⋅ Sylvie Thiebaux
9:15 AM
Hierarchical Reinforcement Learning with AI Planning Models
Junkyu Lee ⋅ Michael Katz ⋅ Don Joven Agravante ⋅ Miao Liu ⋅ Geraud Nangue Tasse ⋅ Tim Klinger ⋅ Shirin Sohrabi Araghi
9:25 AM
Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Stochastic Settings
Rushang Karia ⋅ Pulkit Verma ⋅ Gaurav Vipat ⋅ Siddharth Srivastava
9:35 AM
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
Khimya Khetarpal ⋅ Claire Vernade ⋅ Brendan O'Donoghue ⋅ Satinder Singh ⋅ Tom Zahavy
9:45 AM
A Theoretical Explanation of Deep RL Performance in Stochastic Environments
Cassidy Laidlaw ⋅ Banghua Zhu ⋅ Stuart J Russell ⋅ Anca Dragan
9:55 AM
Lunch Break
12:05 PM
Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
Yu-An Lin ⋅ Chen-Tao Lee ⋅ Guan-Ting Liu ⋅ Pu-Jen Cheng ⋅ Shao-Hua Sun
12:15 PM
PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning
Hao Zhang ⋅ Tianpei Yang ⋅ YAN ZHENG ⋅ Jianye Hao ⋅ Matthew Taylor
12:25 PM
Poster Session 1
1:00 PM
Coffee Break
2:35 PM
RL3: Boosting Meta Reinforcement Learning via RL inside RL2
Abhinav Bhatia ⋅ Samer Nashed ⋅ Shlomo Zilberstein
2:45 PM
Towards General-Purpose In-Context Learning Agents
Louis Kirsch ⋅ James Harrison ⋅ Daniel Freeman ⋅ Jascha Sohl-Dickstein ⋅ Jürgen Schmidhuber
3:25 PM
Closing Remarks
Massively Scalable Inverse Reinforcement Learning for Route Optimization
Matt Barnes ⋅ Matthew Abueg ⋅ Oliver Lange ⋅ Matt Deeds ⋅ Jason Trader ⋅ Denali Molitor ⋅ Markus Wulfmeier ⋅ Shawn O'Banion
Reasoning with Language Model is Planning with World Model
Shibo Hao ⋅ Yi Gu ⋅ Haodi Ma ⋅ Joshua Hong ⋅ Zhen Wang ⋅ Daisy Zhe Wang ⋅ Zhiting Hu
Robustness and Regularization in Reinforcement Learning
Esther Derman ⋅ Yevgeniy Men ⋅ Matthieu Geist ⋅ Shie Mannor
Learning Generalizable Visual Task Through Interaction
Weiwei Gu ⋅ Anant Sah ⋅ Nakul Gopalan
Non-adaptive Online Finetuning for Offline Reinforcement Learning
Audrey Huang ⋅ Mohammad Ghavamzadeh ⋅ Nan Jiang ⋅ Marek Petrik
Learning Interactive Real-World Simulators
Sherry Yang ⋅ Yilun Du ⋅ Kamyar Ghasemipour ⋅ Jonathan Tompson ⋅ Dale Schuurmans ⋅ Pieter Abbeel
Agent-Centric State Discovery for Finite-Memory POMDPs
Lili Wu ⋅ Ben Evans ⋅ Riashat Islam ⋅ Raihan Seraj ⋅ Yonathan Efroni ⋅ Alex Lamb
Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
Ying Fan ⋅ Jingling Li ⋅ Adith Swaminathan ⋅ Aditya Modi ⋅ Ching-An Cheng
Leveraging Behavioral Cloning for Representation Alignment in Cross-Domain Policy Transfer
Hayato Watahiki ⋅ Ryo Iwase ⋅ Ryosuke Unno ⋅ Yoshimasa Tsuruoka
Contrastive Abstraction for Reinforcement Learning
Vihang Patil ⋅ Markus Hofmarcher ⋅ Elisabeth Rumetshofer ⋅ Sepp Hochreiter
Work-in-Progress: Using Symbolic Planning with Deep RL to Improve Learning
Tianpei Yang ⋅ Srijita Das ⋅ Christabel Wayllace ⋅ Matthew Taylor
Plansformer: Generating Symbolic Plans using Transformers
Vishal Pallagani ⋅ Bharath Muppasani ⋅ Keerthiram Murugesan ⋅ Francesca Rossi ⋅ Lior Horesh ⋅ Biplav Srivastava ⋅ Francesco Fabiano ⋅ Andrea Loreggia
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Lukas Schäfer ⋅ Filippos Christianos ⋅ Amos Storkey ⋅ Stefano Albrecht
Towards More Likely Models for AI Planning
Turgay Caglar ⋅ Sirine Belhaj ⋅ Tathagata Chakraborti ⋅ Michael Katz ⋅ Sarath Sreedharan
Learning AI-System Capabilities under Stochasticity
Pulkit Verma ⋅ Rushang Karia ⋅ Gaurav Vipat ⋅ Anmol Gupta ⋅ Siddharth Srivastava
Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran ⋅ Mohamad Hosein Danesh ⋅ Stefano Albrecht ⋅ Sarah Keren
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks
Benedict Quartey ⋅ Ankit Shah ⋅ George Konidaris
Normalization Enhances Generalization in Visual Reinforcement Learning
Lu Li ⋅ Jiafei Lyu ⋅ Guozheng Ma ⋅ Zilin Wang ⋅ Zhenjie Yang ⋅ Xiu Li ⋅ Zhiheng Li
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
David Yunis ⋅ Justin Jung ⋅ Falcon Dai ⋅ Matthew Walter
Contrastive Representations Make Planning Easy
Benjamin Eysenbach ⋅ Vivek Myers ⋅ Sergey Levine ⋅ Russ Salakhutdinov
Inverse Reinforcement Learning with Multiple Planning Horizons
Jiayu Yao ⋅ Finale Doshi-Velez ⋅ Barbara Engelhardt
Stochastic Safe Action Model Learning
Zihao Deng ⋅ Brendan Juba
Learning Discrete Models for Classical Planning Problems
Forest Agostinelli ⋅ Misagh Soltani
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
Omkar Shelke ⋅ Pranavi Pathakota ⋅ Anandsingh Chauhan ⋅ Hardik Meisheri ⋅ Harshad Khadilkar ⋅ Balaraman Ravindran
Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
Jung-Chun Liu ⋅ Chi-Hsien Chang ⋅ Shao-Hua Sun ⋅ Tian-Li Yu
Learning Generalizable Symbolic Options for Transfer in Reinforcement Learning
Rashmeet Kaur Nayyar ⋅ Shivanshu Verma ⋅ Siddharth Srivastava
Inductive Generalization in Reinforcement Learning from Specifications
Rohit kushwah ⋅ Vignesh Subramanian ⋅ Suguman Bansal ⋅ Subhajit Roy
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
Arundhati Banerjee ⋅ Soham Phade ⋅ Stefano Ermon ⋅ Stephan Zheng
Modeling Boundedly Rational Agents with Latent Inference Budgets
Athul Jacob ⋅ Abhishek Gupta ⋅ Jacob Andreas
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
Emily Jin ⋅ Jiaheng Hu ⋅ Zhuoyi Huang ⋅ Ruohan Zhang ⋅ Jiajun Wu ⋅ Fei-Fei Li ⋅ Roberto Martín-Martín
Learning Safe Action Models with Partial Observability
Brendan Juba ⋅ Hai Le ⋅ Ron T Stern
Value Iteration with Value of Information Networks
Samantha Johnson ⋅ Michael Buice ⋅ Koosha Khalvati
Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models
Kevin Black ⋅ Mitsuhiko Nakamoto ⋅ Pranav Atreya ⋅ Homer Walke ⋅ Chelsea Finn ⋅ Aviral Kumar ⋅ Sergey Levine
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Xiyao Wang ⋅ Ruijie Zheng ⋅ Yanchao Sun ⋅ ruonan jia ⋅ Wichayaporn Wongkamjan ⋅ Huazhe Xu ⋅ Furong Huang
General and Reusable Indexical Policies and Sketches
Blai Bonet ⋅ Dominik Drexler ⋅ Hector Geffner
Conservative World Models
Scott Jeen ⋅ Tom Bewley ⋅ Jonathan Cullen
Targeted Uncertainty Reduction in Robust MDPs
Uri Gadot ⋅ Kaixin Wang ⋅ Esther Derman ⋅ Navdeep Kumar ⋅ Kfir Y. Levy ⋅ Shie Mannor
Quantized Local Independence Discovery for Fine-Grained Causal Dynamics Learning in Reinforcement Learning
Inwoo Hwang ⋅ Yun-hyeok Kwak ⋅ Suhyung Choi ⋅ Byoung-Tak Zhang ⋅ Sanghack Lee
Relating Goal and Environmental Complexity for Improved Task Transfer: Initial Results
Sunandita Patra ⋅ Paul Rademacher ⋅ Kristen Jacobson ⋅ Kyle Hassold ⋅ Onur Kulaksizoglu ⋅ Laura Hiatt ⋅ Mark Roberts ⋅ Dana Nau
Uncertainty-Aware Action Repeating Options
Joongkyu Lee ⋅ Seung Joon Park ⋅ Yunhao Tang ⋅ Min-hwan Oh
Robust Driving Across Scenarios via Multi-residual Task Learning
Vindula Jayawardana ⋅ Sirui Li ⋅ Cathy Wu ⋅ Yashar Farid ⋅ Kentaro Oguchi
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
Thomas Jiralerspong ⋅ Flemming Kondrup ⋅ Doina Precup ⋅ Khimya Khetarpal
A Study of Generalization in Offline Reinforcement Learning
Ishita Mediratta ⋅ Qingfei You ⋅ Minqi Jiang ⋅ Roberta Raileanu