Popular GitHub Repositories for Double Auction

强化学习中文教程（蘑菇书?），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

An elegant PyTorch deep reinforcement learning library.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Modularized Implementation of Deep RL Algorithms in PyTorch

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

? Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

DoubleML - Double Machine Learning in Python

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks.

Using tabular and deep reinforcement learning methods to infer optimal market making strategies

A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

Source code for EMNLP 2020 paper: Double Graph Based Reasoning for Document-level Relation Extraction

Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.

An RL model that uses double deep Q learning to generate an optimal policy of stock market trades

PyTorch code for ICPR 2020 paper "DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting"

Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"