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Biological and Artificial Reinforcement Learning
Raymond Chua · Feryal Behbahani · Julie J Lee · Sara Zannone · Rui Ponte Costa · Blake Richards · Ida Momennejad · Doina Precup

Sat Dec 12 04:30 AM -- 03:45 PM (PST) @ None
Event URL: https://sites.google.com/view/biologicalandartificialrl »

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.

Author Information

Raymond Chua (McGill University / Mila)
Feryal Behbahani (DeepMind)
Julie J Lee (University College London)
Sara Zannone (Google DeepMind)
Rui Ponte Costa (University of Bristol)
Blake Richards (Mila)
Ida Momennejad (Columbia University)
Doina Precup (McGill University / Mila / DeepMind Montreal)

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