Papers
Accepted Papers
Accepted Papers
- Learning to Plan with Logical Automata, Brandon Araki, Kiran Vodrahalli, Cristian-Ioan Vasile, and Daniela Rus
- Trajectory-based Probabilistic Policy Gradient for Learning Locomotion Behaviors, Sungjoon Choi and Joohyung Kim
- Online Learning in Kernelized Markov Decision Processes, Sayak Ray Chowdhury and Aditya Gopalan
- Planning and Reinforcement Learning through Approximate Inference and Aggregate Simulation, Hao Cui and Roni Khardon
- Quinoa: a Q-function You Infer Normalized Over Actions, Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, and Martin Riedmiller
- State Aggregation Learning from Markov Transition Data, Yaqi Duan, Zheng Tracy Ke and Mengdi Wang
- VIREL: A Variational Inference Framework for Reinforcement Learning, Matthew G Fellows, Anuj Mahajan, Tim G. J. Rudner, and Shimon Whiteson
- Self-supervised Learning of Image Embedding for Continuous Control, Carlos Florensa, Jonas Degrave, Nicolas Heess, and Martin Riedmiller
- Hierarchical Imitation Learning via Variational Inference of Control Programs, Roy Fox, Richard Shin, William Paul, Yitian Zou, Dawn Song, Ken Goldberg, Pieter Abbeel, and Ion Stoica
- Learning Procedural Abstractions, Karan Goel and Emma Brunskill
- Visual-Based Parameterized Proximal Policy Optimization, Ming-Xu Huang, I-Chen Wu, Bo-Yang Hsueh, Tinghan Wei, and Pei-Shu Huang
- Entropic Policy Composition with Generalized Policy Improvement and Successor Features, Jonathan Hunt, Andre Barreto, Timothy Lillicrap, and Nicolas Heess
- Robot Motion Planning in Learned Latent Spaces, Brian Ichter and Marco Pavone
- Inference and Distillation for Option Learning, Maximilian Igl, Wendelin Boehmer, Andrew Gambardella, Nantas Nardelli, Narayanaswamy Siddharth, and Shimon Whiteson
- Variational Inference Techniques for Sequential Decision Making in Generative Models, Igor Kiselev
- Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems, Arash Mehrjou and Bernhard Schölkopf
- Unsupervised discovery of an agent’s action space via variational future prediction, Karl Pertsch, Oleh Rybkin, Kosta Derpanis, and Andrew Jaegle
- Probabilistic Planning with Sequential Monte Carlo, Alexandre Piché, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, and Christopher Pal
- Deep Imitative Models for Flexible Inference, Planning, and Control, Nicholas Rhinehart, Rowan McAllister, and Sergey Levine
- Inferring Optimal Policies to Satisfy Temporal Logic Functions with Constraint Satisfaction Propagation, Thomas J Ringstrom and Paul Schrater
- Tight Bayesian Ambiguity Sets for Robust MDPs, Reazul Hasan Russel and Marek Petrik
- Variational Auto-encoding Contexts for Control, Yunhao Tang and Xiya Cao
- Learning Robotic Manipulation through Visual Planning and Acting, Angelina Wang, Thanard Kurutach, Aviv Tamar, and Pieter Abbeel
- Inverse POMDP: Inferring What You Think from What You Do, Zhengwei Wu, Paul Schrater, and Xaq Pitkow
- Learning with Stochastic Guidance for Navigation, Linhai Xie, Yishu Miao, Sen Wang, Phil Blunsom, Zhihua Wang, Changhao Chen, Niki Trigoni, and Andrew Markham
- SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning, Marvin Zhang, Sharad Vikram, Laura M Smith, Pieter Abbeel, Matthew J Johnson, and Sergey Levine
- Scalable Thompson Sampling via Optimal Transport, Ruiyi Zhang, Zheng Wen, Changyou Chen, and Lawrence Carin Duke