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ef_informatik:machine-learning [2022-02-07 10:46] – angelegt hofef_informatik:machine-learning [2022-03-29 09:58] (aktuell) – [Syllabus] hof
Zeile 14: Zeile 14:
     * Immediate reward given by game score (points)      * Immediate reward given by game score (points) 
     * Q-Function is a table (Q-Table) dynamically updated by Bellman-formula      * Q-Function is a table (Q-Table) dynamically updated by Bellman-formula 
-  * Hidden Markov Models +  * Hidden Markov Models (a tiny bit of theory)
     * -> what if we cannot observe all the internal state?      * -> what if we cannot observe all the internal state? 
-    * -> infer probability of hidden (latent) state from observations  +    * -> infer probability of hidden (latent) state from observations 
-  * Reinforcement Learning +  * Deep Reinforcement Learning 
     * Q-Function is not a table, but a neural network, which is trained by repeatedly running the game and updating the neuron states.      * Q-Function is not a table, but a neural network, which is trained by repeatedly running the game and updating the neuron states. 
-    * builds on top of sca's NN week+    * builds on top of [[neuronales_netzwerk_handschrift:|sca's NN program]] 
 +    * Prereqs: 
 +      * OpenAI Gym environment: https://gym.openai.com/docs/ 
 +      * keras / tensorflow?
  • ef_informatik/machine-learning.1644230816.txt.gz
  • Zuletzt geändert: 2022-02-07 10:46
  • von hof