0000003356 20W 4SWS VI Advanced Deep Learning for Robotics (IN2349)   Hilfe Logo

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Advanced Deep Learning for Robotics (IN2349) 
lecture with integrated exercises
Winter semester 2020/21
Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
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Angaben zur Abhaltung
This course is 2V + 2P, the practical part always takes place directly after the lecture. The course will be given by B. Bäuml.

Details about the course, esp. about its special digital form for this semester, will be given on the course webpage: https://bbaeuml.github.io/tum-adlr/

This is the advanced deep learning lecture with a specific focus on Robotics and deep reinforcement learning (including a guest lecture from DeepMind).

For the semester long project in the practical part, for each student $1000 credits in the Google Cloud are provided via a Google Educational Grant.

Taking the “Introduction to Deep Learning” course is expected.

1. Introduction & Recap of Deep Learning
2. Advanced Network Architectures & Recursive Neural Networks (LSTMs)
3. Hyperparameter & Architecture Search 

Bayesian optimization, network architecture search (NAS)
4. Adversarial Samples & Adversarial Training
5. Bayesian Deep Learning

Bayesian learning, variational inference, Monte Carlo dropout method
6. Generative Models: VAEs & GANs

variational auto-encoders, generative neural networks (WGAN-GP)
7. Data Efficient Learning: Transfer & Semi-Supervised Learning
8. Deep Reinforcement Learning I

MDPs, Q-iteration, Bellman equation, deep Q-learning, example: Atari-games
9. Deep Reinforcement Learning II

policy gradients, REINFORCE, actor-critic algorithm, TRPO, PPO, robotic applications
10. Deep Reinforcement Learning III

advanced methods: DDPG, soft Q-learning, soft actor-critic (SAC), robotic applications
11. Deep Reinforcement Learning IV

model-based DRL, MCTS + learned heuristics, AlphaZero, model learning, PDDM, robotics
12. Guest Lecture from DeepMind

Recent Developments in Deep Reinforcement Learning for Robotics
IN2346 Introduction to Deep Learning
MA0902 Analysis für Informatiker MA0901 Lineare Algebra für Informatiker
Upon completion of this module, students will have acquired extensive theoretical concepts behind advanced architectures of neural networks and state of the art deep reinforcement learning methods in the context of robotic tasks. In addition to the theoretical foundations, a significant aspect lies on the practical realization of deep reinforcement learning (DRL) methods in robotic scenarios.

The lectures will provide extensive theoretical aspects of advanced deep learning architectures and specifically deep reinforcement learning methods in the field of robotics. The lecture will have reading assignments (e.g., from the DeepLearning book and recent RSS/ICRL/ICRA/IROS papers).

The practical sessions will be key, students shall get familiar with esp. Deep Reinforcement Learning through hours of training and testing. The students will do a semester-long project in teams of 2 with weekly presentations and tutoring of the projects throughout the semester. They will work with TensorFlow and OpenAI Gym and implement advanced deep reinforcement learning methods for state of the art robotic problems. For each student, $1000 credits in the Google Cloud are available via a Google Educational Grant.
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
Note: TUMonline
- Slides given during the course

- I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. MIT Press, 2016. (http://www.deeplearningbook.org)

Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
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course documents
e-learning course (moodle)
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