Displaying 1 to 14 from 14 results

basic_reinforcement_learning - An introduction series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials

  •    Jupyter

This repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques. A quick background review of RL is available here.

dissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

  •    Python

This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.

Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

  •    Jupyter

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

drivebot - RL for driving a rover around

  •    Python

drivebot has two ROS specific components that need to be built. load map and add three bots...

Q-GridWorld - Demo project using tabular Q-learning algorithm

  •    CSharp

Simple Unity project demonstrating the Q-learning algorithm in a tabular setting. For an in-browser WebGL version, follow the link here. In the simplest scenario, we have a 5x5 grid world with an agent (blue block), a goal (green block) and obstacles (red blocks). For each run of the demo, the positions of the agent, goal and obstacles are all selected at random (but remain consistent throughout the same demo run). In this grid world setting, the goal of the agent is to learn a strategy to navigate from its start position to the goal position efficiently while avoiding obstacles. It achieves this by learning the best action to take for every state it is in (typically called a policy in reinforcement learning). An action here is a direction to move (north, south, east and west), while a state here is its position in the grid world. It essentially learns the shortest, obstacle-free path from its start position to the goal position.

ctc-executioner - Master Thesis: Limit order placement with Reinforcement Learning

  •    Jupyter

CTC-Executioner is a tool that provides an on-demand execution/placement strategy for limit orders on crypto currency markets using Reinforcement Learning techniques. The underlying framework provides functionalities which allow to analyse order book data and derive features thereof. Those findings can then be used in order to dynamically update the decision making process of the execution strategy. The methods being used are based on a research project (master thesis) currently proceeding at TU Delft.


  •    Ruby

This example will show how we can teach an AI to play a simple game using the Q-learning reinforcement learning algorithm. This is implemented in pure Ruby without any external dependencies.

dqn - Implementation of q-learning using TensorFlow

  •    Python

An implementation of q algorithm of Reinforcement Learning. decayed ε-greedy exploration, and when exploration, 0.95 probability to do nothing(because in flappy bird, most time wo do nothing). This is very important. It makes model converge in less than 2 hours.

2048-deep-reinforcement-learning - Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning

  •    Jupyter

2048 is a single-player sliding block puzzle game designed by Italian web developer Gabriele Cirulli. The game's objective is to slide numbered tiles on a grid to combine them to create a tile with the number 2048; however, you can keep playing the game, creating tiles with larger numbers. 2048 is played on a gray 4×4 grid, with numbered tiles that slide smoothly when a player moves them using the four arrow keys.Every turn, a new tile will randomly appear in an empty spot on the board with a value of either 2 or 4. Tiles slide as far as possible in the chosen direction until they are stopped by either another tile or the edge of the grid. If two tiles of the same number collide while moving, they will merge into a tile with the total value of the two tiles that collided. The resulting tile cannot merge with another tile again in the same move. Higher-scoring tiles emit a soft glow.

king-pong - Deep Reinforcement Learning Pong Agent, King Pong, he's the best

  •    Python

In this repository, you have an agent that plays the game of pong. Make no mistake though, this is not a normal player. King (the agent) has learned to play the game of pong all by himself, by looking at the screen just like you would. Now, as you can imagine, there are a lot of cutting edge technologies being mixed into this project. First, we have Computer Vision to be able to receive the percepts from the screen. Next, we have Reinforcement Learning which is part of Machine Learning, but it is not classification, nor regression, or clustering. Reinforcement Learning is inspired by the study of animal behavior. In specific, how animals react to pain, reward signals through time. King wants to win, that's why he learns to do what he does.

markovjs - Reinforcement Learning in JavaScript

  •    Javascript

This is a reference implementation of a basic reinforcement learning environment. It is intended as a playground for anyone interested in this field. This package exports a function that provides the environment you'll need to try your own problems.