A-introduction-to-reinforcement-learning

Instructions

- why do I maintain this repository?

Since it's hard for me to find a complete and feasible algorithms set on ‘Reinforcement Learning’ which is of vital help for freshers who want to get into this field.

Nowthat I have made a detailed reading notes on the excellent book "A-introduction-to-reinforcement-learning" and implemented most of its algorithms, why not share them to public?

I believe the quote > 'talk is cheap, show me the code'.

- what are in this repository?

This repository consists of the algorithms from first 9 chapters in this book: Reinforcement Learning:An introduction --2012 version

The algorithms after 9th chapter will be added continuously by another version of this book.

• chapter4 : Dynamic Programming

  Includes two exercises:

  1. The Gambler
  2. Jack's Car Rental

• chapter5 : Monte Carlo Methods

  Includes an exercise called "racetrack" and experiment performances on racetrack.

  1. Racetrack.py

  Note:the Monte Carlo algorithms from chapter 5 are all implemented in the single file Racetrack.py. Specifically in func: def update_policy(episode):

• chapter6 : Temporal-Difference Learning

  Includes td-related algorithms and experiment performances on racetrack.

  1. td.py

  Note: From this chapter, I quit implementing environment of every exercise. Since almost each exercise has a different environment. If I just used different algorithms on different environments, one can hardly has a comparison between those algorithms, Therefore, I decided to show the performances of different algorithms on single same environment: racetrack.

  

• chapter7 : Eligibility Traces

  Includes td-lambda related algorithms and experiment performances on racetrack

  1. td_lambda.py

• chapter8 : Planning and Learning with Tabular Methods

  Includes Dyna_Q algorithm.

  1. Dyna_Q

• RLF ： An reinforcement learning algorithms library which pulls together all algorithms mentioned above and some new deep reinforment learning algorithms like DQN or DDPG for purpose of convenient external call.
  Note: One remarkable point of this library is the seperation of 'environment' and 'agent algorithm' via the attribute of 'python Class'. In this way, you could add or modify your own environment without interfering agent part. Also, you can just connect agent algorithms(td,monte-carlo,dp,...) to your environment.

  • environment code:
    • env.py
  • agent algorithms:
    • monte_carlo.py
    • dp.py
    • td.py
    • td_lambda.py
    • Dyna_Q.py
    • DQN.py
  • main function:
    • main.py

- How to utilize them for your projects?