Deep Reinforcement Learning
Introduction to Deep RL
Introduction
Review
Reinforcement Learning
Deep Learning
Why is deep learning useful for RL?
Challenges of Deep RL
Landscape of methods
Value-Based Methods
Value Function Estimation
Action-Value Function Estimation
Neural-fitted Q iteration (NFQ)
Deep Q Networks (DQN)
DQN Extensions
Double DQN
Dueling DQN
Prioritized Experience Replay
Normalized Advantage Function
Bootstrapped DQN
Applications
TD-Gammon (1992)
Policy Search
Introduction
The REINFORCE gradient estimator
Policy Gradient Extensions
Trust Region Policy Optimization
Natural Policy Gradients
Proximal Policy Optimization
Actor-Critic Paradigm
Distributed RL
Gorila
Advantage Actor-Critic (A2C)
Applications
Robotics
Model Estimation
Introduction
Monte Carlo Tree Search (MCTS)
UCT-to-Realtime
Applications
AlphaGo (2017)
Hierarchical RL
Introduction to Hierarchical RL
Options
Options DQN
Option-Critic Architecture
Hierarchical DQN
Feudal Networks
Inverse RL
Multi-Agent RL
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Actor-Critic Paradigm
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