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Intro and modular design
Replay Memory: Add and use experiences, Benefits
DQNNet: Online, Target and Architecture
DQNAgent: How to act, exploration vs exploitation, Epsilon-greedy
DQNAgent: How to learn, Q-value recap, updating the online and target networks
Walk through of the algorithm presented in the paper
See ya
DQN wins at Breakout
Hello there
Say hello to Garfield
Garfield Gridworld and the need for DQN
But we don’t have a labelled dataset here…
What is a Replay Memory?
What do Q-values represent: Intuition
What do Q-values represent: Formalisation
Wait, Q-values are recursive
NN estimates Q-value
We finally have labels…
Erm, but the targets are estimates… (Bootstrapping)
DQN Paper: 2013 vs 2015 (Introducing the Target Network)
Updated loss function
Don’t forget to explore during training
Let’s summarise this information overload
Don’t forget to check out the blog posts
Seeeeee ya
Introduction
Recap: Reinforcement Learning
Recap: Markov Decision Process
Tabular Q-Learning to Deep Q-Learning
Neural Network Process for DQN
Intuition of Q-learning
Q-Learning as a form of Temporal Difference Learning
How to generate experience: Epsilon-greedy actions
How to learn from experience: Replay buffer
Q-Value
Learning Q-Values
Policy & Target Network
Visualizing Q-Values
Choosing the Optimal Action
Improving DQN
Jupyter Notebook on Cart Pole
High-level overview of the paper
Experience replay buffer
Difficulties with RL (correlations, non-stationary distributions)
DQN is very general
MDP formalism and optimal Q function
Function approximators
The loss function explained
The deadly triad
Algorithm walk-through
Preprocessing and architecture details
Additional details - normalizing score, schedule, etc.
Agent training metrics
Results
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