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2021 RL Virtual School
RLVS home
Classes
Opening remarks
RLVS overview
RL fundamentals
Intro to Deep Learning
Reward Processing Biases in Humans and RL Agents
Introduction to Hierarchical Reinforcement Learning
Stochastic bandits
Monte Carlo Tree Search
Multi-armed bandits in clinical trials
Deep Q-Networks and its variants
Regularized MDPs
Regret bounds of model-based reinforcement learning
Policy Gradients and Actor Critic methods
Pitfalls in Policy Gradient methods
Exploration in Deep RL
Evolutionary Reinforcement Learning
Evolving Agents that Learn More Like Animals
Micro-data Policy Search
Efficient Motor Skills Learning in Robotics
RL tips and tricks
Symbolic representations and reinforcement learning
Leveraging model-learning for extreme generalization
Wrap-up
Speakers
D. A. Berry
K. Chatzilygeroudis
M. Garnelo
M. Geist
L. P. Kaelbling
T. Lattimore
D. Lee
J.-B. Mouret
B. Piot
M. Pirotta
D. Precup
E. Rachelson
A. Raffin
I. Rish
S. Risi
O. Sigaud
C. Tallec
M. Wang
D. Wilson
Organizers
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David Bertoin
David Bertoin
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