Motivation

Reinforcement Learning (RL) algorithms learn how to interact with the environment guided by reward signals. Since its origins, this approach has been greatly inspired by psychology and behavioural sciences, and has been proven to be extremely successful for building autonomous agents. Supplementary to these works, reinforcement learning theory has provided a normative perspective and framework to study how animals and humans learn through rewards, contributing to some of the most important discoveries in the field. With the progression of time, biological and artificial reinforcement learning have contributed to their reciprocal development by informing and inspiring each other. Building on this historical and tight connection, our workshop will bring together a vast array of researchers interested in biological and artificial RL, with the aim of both gaining inspiration from neural and cognitive mechanisms to tackle current challenges in RL research for designing intelligent agents as well as using machine learning theory to further our understanding humans’ or animals’ brain and behaviour.

We invite you to submit papers (up to 5 pages, excluding references and appendix) in the NeurIPS 2019 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 talks or posters. There will be no proceedings for this workshop, however, authors can opt to have their abstracts posted on the workshop website.

In line with the guidelines defined by the NeurIPS organising committee, we can only accept original work that is not published in the main NeurIPS conference. However, we 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/NeurIPSWSBARL2019/

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

ORGANIZERS

Accepted papers

• Humans flexibly transfer options at multiple levels of abstractions. Liyu Xia, Anne Collins
• Slow processes of neurons enable a biologically plausible approximation to policy gradient. Anand Subramoney, Franz Scherr, Guillaume Bellec, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
• MEMENTO: Further Progress Through Forgetting. Liam Fedus, Dibya Ghosh, John Martin, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle
• Goal-directed state space models of animal behavior. Blue Sheffer, Scott Linderman
• Tactile RL-Architecture: a Platform for Robot Haptic Interaction Learning. Alexandra Moringen*, Sascha Fleer*, Guillaume Walck, Helge Ritter
• Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control. Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli, Anil Anthony Bharath, Pierre Richemond
• Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means. Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico, Pierre Richemond, Anil Anthony Bharath
• Curiosity-Driven Multi-Criteria Hindsight Experience Replay. John Lanier, Stephen Mcaleer, Pierre Baldi
• Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Map. Yat Long Lo, Sina Ghiassian
• Biologically inspired architectures for sample-efficient deep reinforcement learning. Pierre Richemond, Arinbjörn Kolbeinsson, Yike Guo
• Iterative Policy-Space Expansion in Reinforcement Learning. Jan Lichtenberg, Özgür Simsek
• Bayesian methods for efficient Reinforcement Learning in tabular problems. Efstratios Markou, Carl Edward Rasmussen
• If MaxEnt RL is the Answer, What is the Question? Ben Eysenbach, Sergey Levine
• Translating from Animal Cognition to AI. Matthew Crosby, Benjamin Beyret, Jose Hernandez-Orallo, Lucy Cheke, Marta Halina, Murray Shanahan
• Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders. Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish
• Learning efficient task-dependent representations with synaptic plasticity. Colin Bredenberg, Eero P. Simoncelli, Cristina Savin
• Hippocampal population representations in reinforced continual learning. Samia Mohinta, Stephane Ciocchi, Rui Ponte Costa
• Rapid learning and efficient exploration by mice navigating a complex maze. Matthew Rosenberg*, Tony Zhang*, Pietro Perona, Markus Meister
• Designing model-based and model-free reinforcement learning tasks without human guidance. Jae Hoon Shin, Jee Hang Lee, Shuangyi Tong, Sang Hwan Kim, Sang Wan Lee
• Sparse Skill Coding: Learning Behavioral Hierarchies with Efficient Codes. Sophia Sanborn, Michael Chang, Sergey Levine, Thomas Griffiths