Motivation

Reinforcement learning (RL) algorithms learn through rewards and a process of trial-and-error. This approach is strongly inspired by the study of animal behaviour and has led to outstanding achievements.

However, artificial agents still struggle with a number of difficulties, such as learning in changing environments and over longer timescales, states abstractions, generalizing and transferring knowledge. Biological agents, on the other hand, excel at these tasks.

The first edition of our workshop last year brought together leading and emerging researchers from Neuroscience, Psychology and Machine Learning to share how neural and cognitive mechanisms can provide insights for RL research and how machine learning advances can further our understanding of brain and behaviour. This year, we want to build on the success of our previous workshop, by expanding on the challenges that emerged and extending to novel perspectives. The problem of state and action representation and abstraction emerged quite strongly last year, so this year's program aims to add new perspectives like hierarchical reinforcement learning, structure learning and their biological underpinnings. Additionally, we will address learning over long timescales, such as lifelong learning or continual learning, by including views from synaptic plasticity and developmental neuroscience.

We are hoping to inspire and further develop connections between biological and artificial reinforcement learning by bringing together experts from all sides and encourage discussions that could help foster novel solutions for both communities.

We invite you to submit papers (up to 6 pages, excluding references and appendix) in the NeurIPS 2020 format. The focus of the work should relate to biological and/or artificial reinforcement learning. The review process will be double-blind and accepted submissions will be presented as virtual talks or posters. There will be no proceedings for this workshop, however, authors can opt to have their abstracts/papers posted on the workshop website.

In line with the guidelines defined by the NeurIPS organising committee, we can only accept work that is not published in the main NeurIPS conference. We also welcome published work from other non-machine learning focused venues, particularly work that has previously appeared in Neuroscience or Cognitive Science venues such as Cosyne, RLDM, CogSci and CCN.

Please submit your papers via the following link: https://cmt3.research.microsoft.com/BARL2020/

For any enquiries please reach out to us at BiologicalArtificialRL@gmail.com

ORGANIZERS

Accepted papers

1. Learning multi-dimensional rules with probabilistic feedback via value-based serial hypothesis testing. Mingyu Song, Yael Niv, Mingbo Cai

2. Language Inference for Reward Learning. Xiang Fu, Tao Chen, Pulkit Agrawal, Tommi Jaakkola

3. Action and Perception as Divergence Minimization. Danijar Hafner, Pedro Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

4. Human versus Machine Attention in Deep Reinforcement Learning Tasks. Ruohan Zhang, Bo Liu, Yifeng Zhu, Sihang Guo, Mary Hayhoe, Dana Ballard, Peter Stone

5. Randomized Value Functions via Posterior State-Abstraction Sampling. Dilip Arumugam, Benjamin Van Roy

6. Energy-Based Models for Continual Learning. Shuang Li, Yilun Du, Gido M van de Ven, Antonio Torralba, Igor Mordatch

7. Pain and Machine Learning. Shakir Mohamed, Daniel Ott

8. Rules warp decision-making. Becket Ebitz, Jiaxin Tu, Benjamin Hayden

9. Self-Activating Neural Ensembles for Continual Reinforcement Learning. Samantha N Powers, Abhinav Gupta

10. A Biologically-Inspired Dual Stream World Model. Arthur W Juliani, Margaret E Sereno

11. Asymmetric and adaptive reward coding arises from normalized reinforcement learning. Kenway Louie

12. Prediction problems inspired by animal learning. Banafsheh Rafiee, Sina Ghiassian, Richard S Sutton, Elliot A Ludvig, Adam White

13. Self-Attention as a Generalization of Attention-Weighted Reinforcement Learning. Aurelio Cortese, Lennart Bramlage

14. Evaluating Agents Without Rewards. Brendon Matusch, Jimmy Ba, Danijar Hafner

15. Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare

16. A localist network explaining motivated and goal-directed planning: GOLSA. Justin M Fine, Joshua Brown

17. Escaping Stochastic Traps with Aleatoric Mapping Agents. Augustine Mavor-Parker, Kimberly Young, Caswell Barry, Lewis Griffin

18. Diversity of discounting horizons explains ramping diversity in dopaminergic neurons. Paul Masset, HyungGoo Kim, Athar Malik, Pol Bech, Naoshige Uchida

19. Adversarial Intrinsic Motivation for Faster Goal-Conditioned Reinforcement Learning. Ishan P Durugkar, Peter Stone, Scott Niekum

20. An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

21. Urgency as the opportunity cost of commitment in biological and machine decision-making. Maximilian Puelma Touzel, Paul Cisek, Guillaume Lajoie

22. Mimicking mammalian navigation in Watermaze using brain-inspired representations. Mandana Samiei, Arna Ghosh, Blake Richards

23. Lambda Successor Return Error. Anthony GX-Chen, Veronica Chelu, Blake Richards, Joelle Pineau