Taipei Tech Deep Reinforcement Learning (深度強化學習)

Deep Reinforcement Learning

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Week	Topic	Learning Objectives	Slides	Code	Updated
	Textbooks and Reference	Udacity, "Deep Reinforcement Learning", GitHub Richard S. Sutton and Andrew G. Barto, “Reinforcement Learning, An Introduction, 2nd Edition” The MIT Press, 2018 David Silver's Reinforcement Learning Course Lex Fridman, Deep Reinforcement Learning, MIT Maxim Lapan, “Deep Reinforcement Learning Hands-on,” Packt, 2018		GitHub
	Course Requirements		pdf		2020/3/5
	Lab 0	PyTorch Tutorial			2020/4/10
1	Introduction to Deep Reinforcement Learning	What is Reinforcement Learning? State, Action, Reward and Policy Value-based vs. Policy-based Learning On-policy vs. Off-policy	pdf		2020/3/20
2	Multi-armed Bandit Problem	ε-greedy Formula Gradient Bandit Problem	pdf		2020/3/22
3	Markov Decision Processes (MDP)	Finite Markov Decision Processes	pdf		2020/3/20
4	OpenAI Gym		pdf		2020/3/27
5	Dynamic Programming	Value Iteration Policy Iteration	pdf	FrozenLake	2020/4/18
	Lab 1	Frozen Lake 8*8		code	2020/4/10
6	Monte Carlo Methods	First-visit Monte Carlo Importance Sampling for Off-policy	pdf	BlackJack	2020/5/1
7	Temporal Difference Learning	Temporal Difference vs. Monte Carlo SARSA Q-learning n-step TD	pdf	CliffWalking	2020/5/22
8	Function Approximation	Approxiamte Large States via Parameterized Functions	pdf	DQN	2020/5/15
	Lab 2	Car Racing		code	2020/4/10
9	Policy Gradient	Policy Gradient Theorem REINFORCE Actor-Critic	pdf	REINFORCE	2020/5/22
10	Model-based Reinforcement Learning	Table Lookup Model Dyna-Q Monte Carlo Tree Search	pdf		2020/5/29
	Lab 3	VizDoom	pdf		2020/4/10
	Final Project	108-2			2020/7/5