DeepHack.RL: Mikhail Burtsev - Model-based reinforcement learning for alternating environments
DeepHack.Turing: Mikhail Burtsev - iPavlov: Conversational Intelligence Project
DeepHack.RL: Andrew Barto - Intrinsically motivated reinforcement learning
DeepHack.RL: Alexey Dosovitskiy - Visuomotor control in 3D environments
DeepHack.RL: Doina Precup - Temporal abstraction in reinforcement learning
Mikhail Burtsev: Deep Pavlov
Moral evolution: Mikhail Burtsev at TEDxParkKultury
DeepHack.RL: Tejas Kulkarni - Revisiting successor representations
DeepHack.RL: Andrey Raigorodskiy - Перспективные исследовательские задачи
DeepHack.RL: Gabriel Synnaeve - E2D2: Episodic exploration for deep deterministic policies
DeepHack.RL: Marc Bellemare - The role of density models in reinforcement learning
DeepHack.Turing: Maksim Kretov - Applications of RL techniques in NLP: An overview
The Deephack and dotChuckles Podcast 1
DLRLSS 2019 - Model-Based RL - Martha White
Model-Based Policy Optimization (ICML Workshops)
DeepHack.RL: Konstantin Vorontsov - Регуляризация матричных разложений
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Model-based Reinforcement Learning Control for Adaptive Optics
DeepHack.Q&A Anatoly Levenchuk – Machine learning engineering
Real-world model-based reinforcement learning with deep neural networks
