loser-why / Reinforcement_Learning

Research repo of RL

Introduction

This is the repo for storing the reviewing note regarding several domains in reinforcement learning

basic_RL

• UCL course on RL led by David Silver
• "Reinforcement Learning An Introduction" by Richard S. Sutton and Andrew G. Barto
• Introduction to Reinforcement Learning with Function Approximation by R.Sutton, NIPS 2015

DRL(Deep RL)

• A Brief Survey of Deep ReinforcementLearning
• Deep Reinforcement Learning Hands-On by Max Lapan

MARL(Multi-Agent RL)

• Multiagent Reinforcement Learning by Daan Bloembergen, Daniel Hennes, Michael Kaisers, Peter Vrancx. ECML, 2013.
• Bus¸oniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multi-agent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Reviews 38(2), 156–172 (2008)

MORL(Multi-objective RL)

This topic is about RL algorithms dealing with the case where learning agent has to learn the preferences among the multiple goals in the environment

• Multiobjective Reinforcement Learning: A Comprehensive Overview by C.Liu et al., 2015
• A Multi-Objective Deep Reinforcement Learning Framework by TT.Nguyen, 2018

safe_reinforcement_learning

This topic is about RL algorithms aims the safe exploration during the early stage in learning process.

• A Comprehensive Survey on Safe Reinforcement Learning by Garcia and Fernandez, 2015
• Bayes Optimisation
• Tutorial on Safe Reinforcement Learning by Felix Berkenkamp, Andreas Krause

transfer_learning

This topic is about the algorithms aims at successfully transferring the knowledge from the source task to the target task to speed-up learning process.

• A Survey on Transfer Learning by Sinno Jialin Pan and Qiang Yang, 2011
• Transfer in Reinforcement Learning: a Framework and a Survey by Alessandro Lazaric, 2013

imitation_learning

Lectures

1. CS 294-112 at UC Berkeley Deep Reinforcement Learning
2. IMITATION LEARNING TUTORIAL at ICML 2018
3. CMU 10703: Deep Reinforcement Learning and Control
   • Lecture 14: Imitation Learning I
   • Lecture 15: Imitation Learning II
4. Imitation Learning for Robotics, Winter 2019, CSC2621

Survey Papers

1. Imitation Learning: A Survey of Learning Methods by Ahmed Hussein et al., 2017
2. Global overview of Imitation Learning by Alexandre Attia and Sharone Dayan, 2018 Jan
3. Learning How to Actively Learn: A Deep Imitation Learning Approach by M. Liu et al., 2018