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Name: Xueyu Wei
Type: User
Bio: yyds
Name: Xueyu Wei
Type: User
Bio: yyds
(AAAI 2018) Action Branching Architectures for Deep Reinforcement Learning
Code accompanying the paper "Action Robust Reinforcement Learning and Applications in Continuous Control" https://arxiv.org/abs/1901.09184
An index of algorithms for offline reinforcement learning (offline-rl)
Vehicle Detection Project
A novel DDPG method with prioritized experience replay (IEEE SMC 2017)
Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow
Continuous control with deep reinforcement learning - Deep Deterministic Policy Gradient (DDPG) algorithm implemented in OpenAI Gym environments
PyTorch implementations of deep reinforcement learning algorithms and environments
PyTorch implementation of the paper "Deep Reinforcement Learning in Large Discrete Action Spaces" (Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin).
Implementation of the algorithm in Python 3, TensorFlow and OpenAI Gym
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
PyTorch implementations of deep reinforcement learning algorithms
A set of Deep Reinforcement Learning Agents implemented in Tensorflow.
The implement of all kinds of dqn reinforcement learning with Pytorch
DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.
DRLib:A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.
Lightweight and scalable deep reinforcement learning using PyTorch. 🔥
Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥
Fast Library for Approximate Nearest Neighbors
A toolkit for developing and comparing reinforcement learning algorithms.
A jax/stax implementation of: Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.
李宏毅《深度强化学习》笔记,在线阅读地址:https://datawhalechina.github.io/leedeeprl-notes/
Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow
An Implementation of Rainbow: Combining Improvements in Deep Reinforcement Learning on Chess Games
Implementation of benchmark RL algorithms
Python Implementation of Reinforcement Learning: An Introduction
Implementation of deep deterministic policy gradient with Wolpertinger
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.