ron-ligsay / Reinforcement-Learning

This is my repo to learn about Reinforcement Algorithms and creating some games as well

Reinforcement Learning Repo

This repo contains my implementations of various reinforcement learning algorithms.

Structure

The repo is structured as follows:

src
├─── Flappy Bird Neat
├─── Mario AI
├─── OpenAI Gym
├─── PPO
└─── Q-Learning

Each folder contains a README.md file that explains the implementation of the algorithm.

Algorithms

Q-Learning

Q-Learning is a model-free reinforcement learning algorithm. It learns the optimal action-value function Q∗(s,a) for a given MDP. It does not require a model (hence the connotation model-free) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

PPO

Proximal Policy Optimization (PPO) is a family of first-order methods that can be used for policy optimization. It is an on-policy, model-free reinforcement learning algorithm which can be used for both continuous and discrete action spaces. PPO is a policy gradient method which is a class of methods that directly optimize the policy function itself.

OpenAI Gym

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.