Mason0629 / DRL

Deep Reinforcement Learning

Deep Reinforcement Learning

1. Overview.

   • Reinforcement Learning [slides] [lecture note] [Video (in Chinese)].

   • Value-Based Learning [slides] [Video (in Chinese)].

   • Policy-Based Learning [slides] [Video (in Chinese)].

   • Actor-Critic Methods [slides] [Video (in Chinese)].

   • AlphaGo [slides] [Video (in Chinese)].

2. TD Learning.

   • Sarsa [slides] [Video (in Chinese)].

   • Q-learning [slides] [Video (in Chinese)].

   • Multi-Step TD Target [slides] [Video (in Chinese)].

3. Advanced Topics on Value-Based Learning.

   • Experience Replay (ER) & Prioritized ER [slides] [Video (in Chinese)].

   • Overestimation, Target Network, & Double DQN [slides] [Video (in Chinese)].

   • Dueling Networks [slides] [Video (in Chinese)].

4. Policy Gradient with Baseline.

   • Policy Gradient with Baseline [slides] [Video (in Chinese)].

   • REINFORCE with Baseline [slides] [Video (in Chinese)].

   • Advantage Actor-Critic (A2C) [slides] [Video (in Chinese)].

   • REINFORCE versus A2C [slides] [Video (in Chinese)].

5. Advanced Topics on Policy-Based Learning.

   • Trust-Region Policy Optimization (TRPO).

   • Policy Network + RNNs.

6. Dealing with Continuous Action Space.

   • Discrete versus Continuous Control. [slides]

   • Deterministic Policy Gradient (DPG) for Continuous Control. [slides]

   • Stochastic Policy Gradient for Continuous Control. [slides]

7. Multi-Agent Reinforcement Learning.

   • Basics and Challenges [slides] [Video (in Chinese)].

   • Centralized VS Decentralized [slides] [Video (in Chinese)].

8. Imitation Learning.

   • Inverse Reinforcement Learning.

   • Generative Adversarial Imitation Learning (GAIL).