Reading Group We have a reading group in place that meets every two Friday in the smARTLab. Date Time Paper 27th September 2019 13.00-14:00 On Unifying Deep Generative Models 13th September 2019 13.00-14:00 Weight Agnostic Neural Networks 30th August 2019 13.00-14:00 Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules 9th August 2019 13.00-14:00 World Models 7th July 2019 13.00-14:00 Neural Logic Reinforcement Learning 7th June 2019 13.00-14:00 Recurrent Value Functions 22nd March 2019 13.00-14:00 Backpropagation through time and the brain 1st February 2019 13.00-14:00 Neural Ordinary Differential Equations 7th December 2018 13.00-14:00 Interactive Perception: Leveraging Action in Perception and Perception in Action 23th November 2018 13.00-14:00 Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images 16th November 2018 13.00-14:00 Learning Robot Tactile Sensing for Object Manipulation 19th October 2018 13.00-14:00 Re-evaluating Evaluation 28th September 2018 13.00-14:00 Building Machines That Learn and Think Like People 6th July 2018 13.00-14:00 Deep Reinforcement Learning that Matters 15th June 2018 13.00-14:00 Observe and Look Further: Achieving Consistent Performance on Atari 1st June 2018 13.00-14:00 Learning Explanatory Rules from Noisy Data 11th May 2018 13.00-14:00 Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning 23rd Mar 2018 13.00-14:00 The Option-Critic Architecture 9th Mar 2018 13.00-14:00 Reinforcement Learning Neural Turing Machines 23rd Feb 2018 13.00-14:00 Efficient behavior learning in human–robot collaboration 26th Jan 2018 11:00-12:00 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes 15th Dec 2017 11:00-12:00 Posterior Sampling for Large Scale Reinforcement Learning 8th Dec 2017 12:00-13:00 A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning 17th Nov 2017 12:00-13:00 Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards