ICLR 2023 Workshop RRL Submissions

Unsupervised Object Interaction Learning with Counterfactual Dynamics Models
Jongwook Choi, Sungtae Lee, Xinyu Wang, Sungryull Sohn, Honglak Lee
- Published: 03 Mar 2023, Last Modified: 02 May 2023
- RRL 2023 Poster
- Readers: Everyone
Chain-of-Thought Predictive Control
Zhiwei Jia, Fangchen Liu, Vineet Thumuluri, Linghao Chen, Zhiao Huang, Hao Su
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Poster
- Readers: Everyone
Learning to Modulate pre-trained Models in RL
Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, Sepp Hochreiter
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Oral
- Readers: Everyone
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
Lina Mezghani, Piotr Bojanowski, Karteek Alahari, Sainbayar Sukhbaatar
- Published: 03 Mar 2023, Last Modified: 18 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Offline Visual Representation Learning for Embodied Navigation
Karmesh Yadav, Ram Ramrakhya, Arjun Majumdar, Vincent-Pierre Berges, Sachit Kuhar, Dhruv Batra, Alexei Baevski, Oleksandr Maksymets
- Published: 03 Mar 2023, Last Modified: 24 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods
Harshit Sikchi, Amy Zhang, Scott Niekum
- Published: 03 Mar 2023, Last Modified: 20 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Self-Generating Data for Goal-Conditioned Compositional Problems
Ying Yuan, Yunfei Li, Yi Wu
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Poster
- Readers: Everyone
Model-Based Adversarial Imitation Learning As Online Fine-Tuning
Rafael Rafailov, Victor Kolev, Kyle Beltran Hatch, John D Martin, Mariano Phielipp, Jiajun Wu, Chelsea Finn
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning
Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D Martin, Mariano Phielipp, Chelsea Finn
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Masked Trajectory Models for Prediction, Representation, and Control
Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Poster
- Readers: Everyone
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Spotlight
- Readers: Everyone
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas, Matthew Hoffman
- Published: 03 Mar 2023, Last Modified: 21 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Synthetic Experience Replay
Cong Lu, Philip J. Ball, Jack Parker-Holder
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Spotlight
- Readers: Everyone
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor
- Published: 03 Mar 2023, Last Modified: 20 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Towards A Unified Agent with Foundation Models
Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin Riedmiller
- Published: 03 Mar 2023, Last Modified: 19 Apr 2023
- RRL 2023 Oral
- Readers: Everyone
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Johann Brehmer, Joey Bose, Pim De Haan, Taco Cohen
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning
Juan Claude Formanek, Callum Rhys Tilbury, Jonathan Phillip Shock, Kale-ab Tessera, Arnu Pretorius
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Oral
- Readers: Everyone
Beyond Temporal Credit Assignment in Reinforcement Learning
Sephora Madjiheurem, Kim Stachenfeld, Peter Battaglia, Jessica B Hamrick
- Published: 03 Mar 2023, Last Modified: 20 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration
Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt
- Published: 03 Mar 2023, Last Modified: 20 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney
- Published: 03 Mar 2023, Last Modified: 12 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Poster
- Readers: Everyone
Deep Reinforcement Learning with Plasticity Injection
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Spotlight
- Readers: Everyone
On The Role of Forgetting in Fine-Tuning Reinforcement Learning Models
Maciej Wolczyk, Bartłomiej Cupiał, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
- Published: 03 Mar 2023, Last Modified: 20 Apr 2023
- RRL 2023 Poster
- Readers: Everyone
Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Daniel Lawson, Ahmed H Qureshi
- Published: 03 Mar 2023, Last Modified: 14 Apr 2024
- RRL 2023 Spotlight
- Readers: Everyone