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Advanced Deep Learning for Robotics (IN2349)

Course 0000003810 in SS 2023

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
Lecturers Berthold Bäuml
Responsible/Coordination: Darius Burschka
Dates Thu, 12:00–14:00, MI HS2

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks This course is 2V + 2P, the practical part is a semester long project. The course will be given by B. Bäuml. Details about the course will be given on the course webpage: 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 applications 12. Guest Lecture from DeepMind
 Recent Developments in Deep Reinforcement Learning for Robotics
Links Course documents
E-Learning course (e. g. Moodle)
Additional information
TUMonline entry
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