| Description
| The goal of this course is to introduce fundamental theories behind deep reinfocement learning and summarize the state-of-the-art research results. We will also teach how to use Python deep learning framework (TensorFlow & PyTorch) and demonstrate how to train software agents to learn different tasks. The content includes:
1.Introduction to Reinforcement Learning
2.Finite Markov Decision Processes
3.Monte Carlo Methods
4.Temporal-Difference Learning
5.Deep Q-Networks (DQN)
6.OpenAI Gym
7.Actor-Critic Methods (A3C & A2C)
8.Trust Regions – TRPO, PPO and ACKTR
|
|---|