Ecological Theory of RL

ORAL Presentations

1. Understanding the Effects of Dataset Composition on Offline Reinforcement Learning. Kajetan Schweighofer; Markus Hofmarcher; Marius-Constantin Dinu; Angela Bitto; Philipp Renz; Vihang Patil; Sepp Hochreiter

2. HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. Ziniu Li; Yingru Li; Yushun Zhang; Tong Zhang; Zhiquan Luo

3. Grounding an Ecological Theory of Artificial Intelligence in Human Evolution. Eleni Nisioti; Katia Jodogne--del litto; Clément Moulin-Frier

4. Representation Learning for Online and Offline RL in Low-rank MDPs. Masatoshi Uehara; Xuezhou Zhang; Wen Sun

5. Habitat 2.0: Training Home Assistants to Rearrange their Habitat. Andrew Szot; Alexander Clegg; Eric Undersander; Erik Wijmans; Yili Zhao; John Turner; Noah D. Maestre; Mustafa Mukadam; Devendra Chaplot; Oleksandr Maksymets; Aaron K. Gokaslan; Vladimir Vondrus; Sameer Dharur; Franziska Meier; Wojciech Galuba; Angel X Chang; Zsolt Kira; Vladlen Koltun; Jitendra Malik; Manolis Savva; Dhruv Batra

PosterS

1. Continual Learning In Environments With Polynomial Mixing Times. Matthew D. Riemer; Sharath Chandra Raparthy; Ignacio Cases; Gopeshh Subbaraj; Maximilian Puelma Touzel; Irina Rish

2. Reducing the Information Horizon of Bayes-Adaptive Markov Decision Processes via Epistemic State Abstraction. Dilip Arumugam; Satinder Singh

3. Understanding the Effects of Dataset Composition on Offline Reinforcement Learning. Kajetan Schweighofer; Markus Hofmarcher; Marius-Constantin Dinu; Angela Bitto; Philipp Renz; Vihang Patil; Sepp Hochreiter

4. Behavior Predictive Representations for Generalization in Reinforcement Learning. Siddhant Agarwal; Aaron Courville; Rishabh Agarwal

5. Bayesian Active Reinforcement Learning. Viraj Mehta; Biswajit Paria; Jeff Schneider; Willie Neiswanger.

6. Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. Andrew J Wagenmaker; Kevin Jamieson; Max Simchowitz

7. Reward and State Design: Towards Reinforcement Teaching. Alex Lewandowski; Calarina N. Muslimani; Matthew Taylor; Jun Luo

8. A Deep Q-Network approach for stock management optimization. Reda Alami; Marie-Belle Badr

9. HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. Ziniu Li; Yingru Li; Yushun Zhang; Tong Zhang; Zhiquan Luo

10. Grounding an Ecological Theory of Artificial Intelligence in Human Evolution. Eleni Nisioti; Katia Jodogne--del litto; Clément Moulin-Frier

11. CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. Carolin Benjamins; Theresa Eimer; Frederik Schubert; André Biedenkapp; Bodo Rosenhahn; Frank Hutter; Marius Lindauer

12. Representation Learning for Online and Offline RL in Low-rank MDPs. Masatoshi Uehara; Xuezhou Zhang; Wen Sun

13. Learning Representations for Pixel-based Control: What Matters and Why?. Manan Tomar; Utkarsh A Mishra; Amy Zhang; Matthew E. Taylor

14. Stateful Offline Contextual Policy Evaluation and Learning. Angela Zhou

15. Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions. Siddartha Devic; Zihao Deng; Brendan Juba

16. Habitat 2.0: Training Home Assistants to Rearrange their Habitat. Andrew Szot; Alexander Clegg; Eric Undersander; Erik Wijmans; Yili Zhao; John Turner; Noah D. Maestre; Mustafa Mukadam; Devendra Chaplot; Oleksandr Maksymets; Aaron K. Gokaslan; Vladimir Vondrus; Sameer Dharur; Franziska Meier; Wojciech Galuba; Angel X Chang; Zsolt Kira; Vladlen Koltun; Jitendra Malik; Manolis Savva; Dhruv Batra

17. The Impact of Batch Learning in Stochastic Bandits. Danil Provodin; Pratik Gajane; Mykola Pechenizkiy; Maurits Kaptein [Poster]

18. Unsupervised Reinforcement Learning for Partially Observable Environments Using External Memory. Mitsuhiko Nakamoto; Yoshimasa Tsuruoka

19. Hyperparameters in Contextual RL are Highly Situational. Theresa Eimer; Carolin Benjamins; Marius Lindauer

20. More Efficient Adversarial Imitation Learning Algorithms With Known and Unknown Transitions. Tian Xu; Ziniu Li; Yang Yu

21. Model-based Distributional Reinforcement Learning for Risk-sensitive Control. Hao Liang; Zhiquan Luo [Supplementary]

22. Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning. Kushal Chauhan; Soumya Chatterjee; Pradeep Shenoy; Balaraman Ravindran