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RLVS 2021 - Day 6 - RLVS Wrap-Up
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Reinforcement Learning Virtual School 2021
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- 1 RLVS 2021 - Day 1 - Opening remarks
- 2 RLVS 2021 - Day 1 - Overview
- 3 RLVS 2021 - Day 1 - Fundamentals
- 4 RLVS 2021 - Day 1 - Introduction to deep learning
- 5 RLVS 2021 - Day 1 - Reward processing biases in humans and RL agents
- 6 RLVS 2021 - Day 1 - Introduction to hierarchical reinforcement learning
- 7 RLVS 2021 - Day 2 - Stochastic Bandits
- 8 RLVS 2021 - Day 2 - Monte Carlo Tree Search
- 9 RLVS 2021 - Day 2 - Multi armed bandits in clinical trials
- 10 RLVS 2021 - Day 3 - Deep Q-Networks and its variants (Part 1)
- 11 RLVS 2021 - Day 3 - Deep Q-Networks and its variants (Part 2)
- 12 RLVS 2021 - Day 3 - Deep Q-Networks and its variants (Part 3)
- 13 RLVS 2021 - Day 3 - Regularized MDPs
- 14 RLVS 2021 - Day 3 - Regret bounds of model-based reinforcement learning
- 15 RLVS 2021 - Day 4 - Policy gradients and actor-critic methods
- 16 RLVS 2021 - Day 4 - Pitfalls in policy gradient methods
- 17 RLVS 2021 - Day 5 - Evolutionary Reinforcement Learning
- 18 RLVS 2021 - Day 5 - Evolving agents that learn more like animals
- 19 RLVS 2021 - Day 5 - Micro-data policy search
- 20 RLVS 2021 - Day 5 - Efficient motor skills learning in robotics
- 21 RLVS 2021 - Day 6 - RL in practice: tips & tricks and practical session with stable-baselines3
- 22 RLVS 2021 - Day 6 - Symbolic representations and reinforcement learning
- 23 RLVS 2021 - Day 6 - Leveraging model-learning for extreme generalization
- 24 RLVS 2021 - Day 6 - RLVS Wrap-Up
