Deep Reinforcement Learning, Summer 2019 (Samsung)

This repository contains codes for Deep Reinforcement Learning (DRL) algorithms with PyTorch (v0.4.1). It also provides lecture slides that explain codes in detail.

The agents with the DRL algorithms have been implemented and trained using classic control environments in OpenAI Gym.

• CartPole
• Pendulum

Table of Contents

00. Prerequisite

1. Install from Anaconda to OpenAI Gym (Window Ver. & MacOS Ver.)
2. Numpy

01. Deep Learning with PyTorch

• Slide
• Code

02. Deep Q-Network (DQN) & Double DQN (DDQN)

• Slide
• DQN Code
• DDQN Code

03. Advantage Actor-Critic (A2C) & Deep Deterministic Policy Gradient (DDPG)

1. A2C

   • Slide
   • Code

2. DDPG

   • Slide
   • Code

04. Trust Region Policy Optimization (TRPO) & Proximal Policy Optimization (PPO)

1. TRPO

   • Slide
   • Code

2. TRPO + GAE

   • Slide
   • Code

3. PPO

   • Code

4. PPO + GAE

   • Slide
   • Code

05. Soft Actor-Critic (SAC)

• Slide
• Code

Learning curve

CartPole

Pendulum

Paper

• Deep Q-Network (DQN)
• Double DQN (DDQN)
• Advantage Actor-Critic (A2C)
• Asynchronous Advantage Actor-Critic (A3C)
• Deep Deterministic Policy Gradient (DDPG)
• Trust Region Policy Optimization (TRPO)
• Generalized Advantage Estimator (GAE)
• Proximal Policy Optimization (PPO)
• Soft Actor-Critic (SAC)

Reference

• Minimal and Clean Reinforcement Learning Examples in PyTorch
• Pytorch implementation for Policy Gradient algorithms (REINFORCE, NPG, TRPO, PPO)
• Pytorch implementation of SAC1
• Pytorch implementation of SAC2