Unsupervised Reinforcement Learning @ ICML 2021
Unsupervised learning (UL) has begun to deliver on its promise in the recent past with tremendous progress made in the fields of natural language processing and computer vision whereby large scale unsupervised pre-training has enabled fine-tuning to downstream supervised learning tasks with limited labeled data. This is particularly encouraging and appealing in the context of reinforcement learning considering that it is expensive to perform rollouts in the real world with annotations either in the form of reward signals or human demonstrations. We therefore believe that a workshop in the intersection of unsupervised and reinforcement learning (RL) is timely and we hope to bring together researchers with diverse views on how to make further progress in this exciting and open-ended subfield.
|Paper Submission Deadline||June 9, 2021 AoE|
|Decision Notifications||June 30, 2021|
|Camera Ready Paper Deadline||July 17, 2021 AoE|
|Workshop||July 24, 2021|
Call for Papers
We invite both short (4 page) and long (8 page) anonymized submissions in the ICML LaTeX format that study questions regarding the best ways of combining unsupervised learning with RL. More concretely, we welcome submissions around, but not necessarily limited to, the following broad questions:
- How can the use of UL advance RL?
- What are the most effective ways of combining UL with RL?
- What are the settings in which UL can be most beneficial in RL?
- How is Representation Learning for RL different from downstream supervised tasks?
- What theoretical guarantees can be derived for unsupervised exploration and representation learning in RL?
- How can UL improve RL in terms of sample efficiency, generalization, exploration?
- How can UL and Skill Discovery be maximally synergetic?
- How does the role of UL differ across Model-based RL, Model-free On-policy RL, Model-free Off-policy RL, Offline RL?
- What inspirations can we take from cognitive science to bridge to inspire the next crop of UL methods for RL?
- Is there a unified view to combine different UL methods into a single framework?
This workshop will bring together researchers working in unsupervised learning (including those in computer vision or natural language processing), representation learning and reinforcement learning to discuss the benefits, challenges and potential solutions for effectively using unsupervised learning techniques to enhance reinforcement learning agents. Early workshops were crucial to accelerate the use of UL techniques in vision and language, and we hope this workshop will serve as the kindling for UL techniques in RL.
Note that as per ICML guidelines, we don't accept works previously published in other conferences on machine learning, but are open to works that are currently under submission to a conference (such as NeurIPS 2021).
Submissions should be uploaded on OpenReview: URL submission link.
In case of any issues or questions, feel free to email the workshop organizers at: firstname.lastname@example.org.
Maryland College Park
University of Toronto
NYU / FAIR
For decades unsupervised learning (UL) has promised to drastically reduce our reliance on supervision and reinforcement. Now, in the last couple of years, unsupervised learning has been delivering on this problem with substantial advances in computer vision (e.g., CPC , SimCLR , MoCo , BYOL ) and natural language processing (e.g., BERT , GPT-3 , T5 , Roberta ). The general purpose representations learned by unsupervised methods are useful for a variety of downstream supervised learning tasks, particularly in the low data regime (BERT , GPT-3 , T5 , CPCv2 , SimCLR , SimCLRv2 ).
However, in the context of reinforcement learning, we haven’t seen the level of impact UL has had in vision and language. This is not for the lack of trying. There has been a wide variety of methods developed by the Machine Learning community to use UL to make a meaningful impact in RL. A few prominent directions are as follows:
- Learning rich representations of high dimensional observations to aid reinforcement learning (UNREAL , DARLA , TCN , SAC-AE , SLAC , CURL , DrQ , RAD , ATC , Bisimulation , Proto-RL ).
- Building world models for planning (Visual MPC , Simple , PlaNet , Dreamer , MuZero , CFM ).
- Learning to explore environments with sparse reward signals (EX2 , Curiosity , RND ).
- Learning task agnostic, diverse and reusable skills (VIC , VALOR , DIAYN , DADS ).
- Extracting signals for free with goal-conditioned and hindsight models (UVFA , HER , Asymmetric Self-Play , RIG , Learning From Play ).
- Unsupervised Learning in the context of Meta/Multi-Task Learning (CARML , UML ).
- Sample complexity bounds for unsupervised exploration and representation learning in RL (FLAMBE , BMDP , MaxEnt exploration , DisCO , reward free exploration , Francis ) .
McGill University / Mila / FAIR
NYU / FAIR
McGill University / Mila / FAIR
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- Lynch et al. "Learning Latent Plans from Play." CoRL (2019).
- Jabri et al. "Unsupervised Curricula for Visual Meta-Reinforcement Learning." NeurIPS (2019).
- Gupta et al. "Unsupervised Meta-Learning for Reinforcement Learning." ICLR (2019).
- Yan et al. "Learning Predictive Representations for Deformable Objects Using Contrastive Estimation." CoRL (2020).
- Agarwal et al. "FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs." NeurIPS (2020).
- Feng et al. "Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning." NeurIPS (2020).
- Tarbouriech et al. "Improved Sample Complexity for Incremental Autonomous Exploration in MDPs." NeurIPS (2020).
- Jin et al. "Reward-Free Exploration for Reinforcement Learning." ArXiv (2020).
- Zanette et al. "Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration." NeurIPS (2020).
- Hazan et al. "Provably Efficient Maximum Entropy Exploration." ArXiv (2020).