Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 6

We will combine Nueral Network(NN) features on RL

Basic Deep Neural Network

DNN is linear neural network structure with more than three hidden layers of functional operators which are differentiable. Benefits of using DNN are as below

• DNN is universal function approximator
• Requires less nodes/parameters to represent same function
• Uses distributed representations instead of locqal representations
• Can learn parameters using SGD

I will skip Convolutional Neural Network section

너무 많이 했어...

Deep Q Learning → Deep Q Network(DQN)

We will use DNN to represent Value function, Polcy, and Model and optimize loss finction(SGD)

state-action value function by Q-network with $w$ → $\hat{Q}(s,a;w) \approx Q(s,a)$

With statement above, let us recall State-Action Value Function Approximation and Model-Free Control from **Lecture 5.**

<aside> 💡 Lecture 5 숙지 이후 공부할 것.

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