Applied Deep Learning 2024 - Lecture 6 - Deep Reinforcement Learning

In this lecture, we're looking at Deep Reinforcement Learning and how machines can learn from experiences and navigate in an environment where you only have limited knowledge. Topics include rewards, the value function, policies, and how deep learning can be used to learn a policy function.
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== Literature ==
1. Amini, Soleimany, MIT 6.S191 - Introduction to Deep Learning. introtodeeplear...
2. rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch: arxiv.org/abs/...
3. A Survey of Reinforcement Learning Informed by Natural Language: arxiv.org/abs/1...
4. Stanford CS231n: Reinforcement Learning in Visual Computing • Lecture 14 | Deep Rein...
5. Stanford CS234: Reinforcement Learning • Stanford CS234: Reinfo...
6. Deep Mind: Reinforcement Learning with Unsupervised Auxiliary Tasks, 2016 arxiv.org/abs/...
7. Arxiv Insights: Overcoming spare rewards in Deep RL, 2018. • Reinforcement Learning...
8. Pathak et al. Curiosity-driven Exploration by Self-supervised predicition, 2017. arxiv.org/abs/...
9. Andrychowicz et al. Hindsight Experience Replay, 2017. arxiv.org/abs/...
10. AlphaStar: Mastering the Real-Time Strategy Game StarCraft II: deepmind.com/b...
11. Mnih et al. Human-level control through deep reinforcement learning, 2015. www.nature.com...
12. Silver et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play, 2018. science.scienc...
13. Wang et al. Dueling Network Architectures for Deep Reinforcement Learning, 2016. arxiv.org/abs/...
14. Karpathy, Deep Reinforcement learning: Pong from Pixels, 2016. karpathy.github...