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Last update: doc. Mgr. Barbora Vidová Hladká, Ph.D. (25.01.2019)
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Last update: doc. RNDr. Vladislav Kuboň, Ph.D. (05.06.2018)
The goal of the course is to introduce reinforcement learning combined with deep neural networks. The course will focus both on theory as well as on practical aspects. |
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Last update: doc. RNDr. Vladislav Kuboň, Ph.D. (05.06.2018)
Students pass the practicals by submitting sufficient number of assignments. The assignments are announced regularly the whole semester and are due in several weeks. Considering the rules for completing the practicals, it is not possible to retry passing it. Passing the practicals is not a requirement for going to the exam. |
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Last update: RNDr. Milan Straka, Ph.D. (10.05.2022)
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Last update: RNDr. Milan Straka, Ph.D. (15.06.2020)
The exam is written and consists of questions randomly chosen from a publicly known list. The requirements of the exam correspond to the course syllabus, in the level of detail which was presented on the lectures. |
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Last update: RNDr. Milan Straka, Ph.D. (10.05.2020)
Reinforcement learning framework
Tabular methods
Functional Approximation
Deep Q networks
Policy gradient methods
Continuous action domain
Monte Carlo tree search
Model-based algorithms
Partially observable environments
Discrete variable optimization |
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Last update: doc. RNDr. Vladislav Kuboň, Ph.D. (05.06.2018)
Python programming skills and Tensorflow skills (or any other deep learning framework) are required, to the extent of the NPFL114 course. No previous knowledge of reinforcement learning is necessary. |