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Workshop
Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West Ballroom C
Biological and Artificial Reinforcement Learning
Raymond Chua · Sara Zannone · Feryal Behbahani · Rui Ponte Costa · Claudia Clopath · Blake Richards · Doina Precup





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Reinforcement learning (RL) algorithms learn through rewards and a process of trial-and-error. This approach was strongly inspired by the study of animal behaviour and has led to outstanding achievements in machine learning (e.g. in games, robotics, science). However, artificial agents still struggle with a number of difficulties, such as sample efficiency, learning in dynamic environments and over multiple timescales, generalizing and transferring knowledge. On the other end, biological agents excel at these tasks. The brain has evolved to adapt and learn in dynamic environments, while integrating information and learning on different timescales and for different duration. Animals and humans are able to extract information from the environment in efficient ways by directing their attention and actively choosing what to focus on. They can achieve complicated tasks by solving sub-problems and combining knowledge as well as representing the environment in efficient ways and plan their decisions off-line. Neuroscience and cognitive science research has largely focused on elucidating the workings of these mechanisms. Learning more about the neural and cognitive underpinnings of these functions could be key to developing more intelligent and autonomous agents. Similarly, having a computational and theoretical framework, together with a normative perspective to refer to, could and does contribute to elucidate the mechanisms used by animals and humans to perform these tasks. Building on the connection between biological and artificial reinforcement learning, our workshop will bring together leading and emergent researchers from Neuroscience, Psychology and Machine Learning to share: (i) how neural and cognitive mechanisms can provide insights to tackle challenges in RL research and (ii) how machine learning advances can help further our understanding of the brain and behaviour.

Opening Remarks (Talk)
Raymond Chua, Feryal Behbahani, Sara Zannone, Rui Ponte Costa, Claudia Clopath, Doina Precup, Blake Richards
Invited Talk #1: From brains to agents and back (Talk)
Jane Wang
Coffee Break & Poster Session (Poster Session)
Samia Mohinta, Andrea Agostinelli, Alexandra Moringen, Jee Hang Lee, Richie Lo, Wolfgang Maass, Blue Sheffer, Colin Bredenberg, Benjamin Eysenbach, Liyu Xia, Efstratios Markou, Malte Lichtenberg, Pierre Richemond, Tony Zhang, J.B. Lanier, Baihan Lin, Liam Fedus, Glen Berseth, Marta Sarrico, Matthew Crosby, Stephen McAleer, Sina Ghiassian, Franz Scherr, Guillaume Bellec, Darjan Salaj, Arinbjörn Kolbeinsson, Matthew Rosenberg, Jaehoon Shin, Sang Wan Lee, Guillermo Cecchi, Irina Rish, Elias Hajek
Contributed Talk #1: Humans flexibly transfer options at multiple levels of abstractions (Talk)
Liyu Xia
Contributed Talk #2: Slow processes of neurons enable a biologically plausible approximation to policy gradient (Talk)
Wolfgang Maass
Invited Talk 2: Understanding information demand at different levels of complexity (Talk)
Jacqueline Gottlieb
Invited Talk #3: Predictive Cognitive Maps with Multi-scale Successor Representations and Replay (Talk)
Ida Momennejad
Lunch Break & Poster Session
Invited Talk #4: Multi-Agent Interaction and Online Optimization in RL (Talk)
Igor Mordatch
Invited Talk #5 : Materials Matter: How biologically inspired alternatives to conventional neural networks improve meta-learning and continual learning (Talk)
Jeff Clune
Invited Talk #6: Features or Bugs: Synergistic Idiosyncrasies in Human Learning and Decision-Making (Talk)
Angela Yu
Coffee Break & Poster Session (Poster Session)
Contributed Talk #3 MEMENTO: Further Progress Through Forgetting (Talk)
Liam Fedus
Invited Talk #7: Richard Sutton (Talk)
Richard Sutton
Panel Discussion led by Grace Lindsay (Discussion Panel)
Grace Lindsay, Blake Richards, Doina Precup, Jacqueline Gottlieb, Jeff Clune, Jane Wang, Richard Sutton, Angela Yu, Ida Momennejad