Displaying 1 to 19 from 19 results

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

  •    Python

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

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.




gym-starcraft - StarCraft environment for OpenAI Gym, based on Facebook's TorchCraft. (In progress)

  •    Python

Gym StarCraft is an environment bundle for OpenAI Gym. It is based on Facebook's TorchCraft, which is a bridge between Torch and StarCraft for AI research.Install OpenAI Gym and its dependencies.

rl-portfolio-management - Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv

  •    Jupyter

Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. This paper trains an agent to choose a good portfolio of cryptocurrencies. It's reported that it can give 4-fold returns in 50 days and the paper seems to do all the right things so I wanted to see if I could acheive the same results.

chainer_pong - learn pong by chainer

  •    Python

DQN implementation by Chainer. It iterators 5 episode. If you store the model on ./store directory, that is loaded. You can use trained model that are located in trained_model directory (it is stored by Git LFS, storing latest 5 model). Please copy it to /store directory then run script.


banana-gym - A simple stochastic OpenAI environment for training RL agents

  •    Python

This repository contains a PIP package which is an OpenAI environment for simulating an enironment in which bananas get sold. Install the OpenAI gym.

rlflow - A TensorFlow-based framework for learning about and experimenting with reinforcement learning algorithms

  •    Python

A framework for learning about and experimenting with reinforcement learning algorithms. It is built on top of TensorFlow and interfaces with OpenAI gym (universe should work, too). It aims to be as modular as possible so that new algorithms and ideas can easily be tested. I started it to gain a better understanding of core RL algorithms and maybe it can be useful for others as well. Works with any OpenAI gym environment.

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.

babyai - BabyAI platform

  •    Python

A platform for simulating language learning with a human in the loop. This is a on-going research project based at Mila. Start by manually installing PyTorch. See the PyTorch website for installation instructions specific to your platform.

tf-a3c-gpu - Tensorflow implementation of A3C algorithm

  •    Python

Tensorflow implementation of A3C algorithm using GPU (haven't tested, but it would be also trainable with CPU). On the original paper, "Asynchronous Methods for Deep Reinforcement Learning", suggests CPU only implementations, since environment can only be executed on CPU which causes unevitable communication overhead between CPU and GPU otherwise.

gym-nes-mario-bros - 🐍 🏋 OpenAI GYM for Nintendo NES emulator FCEUX and 1983 game Mario Bros

  •    Python

You can use a virtualenv or a pipenv if you want to install the dependencies in an isolated environment. An implementation of dqn is in src/dqn, using keras.

gym-duckietown - Self-driving car simulator for the Duckietown universe

  •    Python

Duckietown self-driving car simulator environments for OpenAI Gym. This simulator was created as part of work done at Mila.

gym-minigrid - Minimalistic gridworld environment for OpenAI Gym

  •    Python

There are other gridworld Gym environments out there, but this one is designed to be particularly simple, lightweight and fast. The code has very few dependencies, making it less likely to break or fail to install. It loads no external sprites/textures, and it can run at up to 5000 FPS on a Core i7 laptop, which means you can run your experiments faster. A known-working RL implementation can be found in this repository. This environment has been built as part of work done at the MILA.

Hands-On-Intelligent-Agents-with-OpenAI-Gym - Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch

  •    Python

HOIAWOG!: Your guide to developing AI agents using deep reinforcement learning. Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator.

Deep-Q-Networks - Implementation of Deep/Double Deep/Dueling Deep Q networks for playing Atari games using Keras and OpenAI gym

  •    Python

Simply replace --model convnet with --model dueling_convnet in the above command. Also try out other network architectures in deeprl/networks.py. Following curves compare the dueling (yellow), double (green) and simple (blue) deep Q networks.