iuhhhisme

5G-Traffic-Generator

baidu-allreduce

ByteScheduler

A high performance and generic framework for distributed DNN training

corundum

Open source FPGA-based NIC and platform for in-network compute

CSUthesis

中南大学研究生学位论文LaTex模版（博士和硕士）

Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

DynMORL

Code for Dynamic Weights in Multi-Objective Deep Reinforcement Learning

free-programming-books-zh_CN

:books: 免费的计算机编程类中文书籍，欢迎投稿

gym-mo

Simulation environments for Multi-Objective Reinforcement Learning (MORL)

Interactive-Multi-objective-Reinforcement-Learning

Multi-objective reinforcement learning deals with finding policies for tasks where there are multiple distinct criteria to optimize for. Since there may be trade-offs between the criteria, there does not necessarily exist a globally best policy; instead, the goal is to find Pareto optimal policies that are the best for certain preference functions. The Pareto Q-learning algorithm looks for all Pareto optimal policies at the same time. Introduced a variant of Pareto Q-learning that asks queries to a user, who is assumed to have an underlying preference function and also the scalarized Q-learning algorithm which reduces the dimensionality of multi-objective space by using scalarization function and ask user preferences by taking weights for scalarization. The goal is to find the optimal policy for that user’s preference function as quickly as possible. Used two benchmark problems i.e. Deep Sea Treasure and Resource Collection for experiments.

iuhhhisme/5G-Traffic-Generator

iuhhhisme/baidu-allreduce

iuhhhisme/ByteScheduler

A high performance and generic framework for distributed DNN training

iuhhhisme/corundum

Open source FPGA-based NIC and platform for in-network compute

iuhhhisme/CSUthesis

中南大学研究生学位论文LaTex模版（博士和硕士）

iuhhhisme/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

iuhhhisme/DynMORL

Code for Dynamic Weights in Multi-Objective Deep Reinforcement Learning

iuhhhisme/free-programming-books-zh_CN

:books: 免费的计算机编程类中文书籍，欢迎投稿

iuhhhisme/gym-mo

Simulation environments for Multi-Objective Reinforcement Learning (MORL)

iuhhhisme/Interactive-Multi-objective-Reinforcement-Learning

Multi-objective reinforcement learning deals with finding policies for tasks where there are multiple distinct criteria to optimize for. Since there may be trade-offs between the criteria, there does not necessarily exist a globally best policy; instead, the goal is to find Pareto optimal policies that are the best for certain preference functions. The Pareto Q-learning algorithm looks for all Pareto optimal policies at the same time. Introduced a variant of Pareto Q-learning that asks queries to a user, who is assumed to have an underlying preference function and also the scalarized Q-learning algorithm which reduces the dimensionality of multi-objective space by using scalarization function and ask user preferences by taking weights for scalarization. The goal is to find the optimal policy for that user’s preference function as quickly as possible. Used two benchmark problems i.e. Deep Sea Treasure and Resource Collection for experiments.

iuhhhisme/mo-gym

Multi-objective gym environments for reinforcement learning.

iuhhhisme/MORL

Multi-Objective Reinforcement Learning

iuhhhisme/MORL-1

multi-objective reinforcement learning using ddpg

iuhhhisme/mpitutorial

MPI programming lessons in C and executable code examples

iuhhhisme/multi-objective-deep-rl

Multi-Objective Deep Reinforcement Learning

iuhhhisme/ns-3-vr-app

An implementation of a traffic model for VR applications on ns-3

iuhhhisme/SmartNIC-GEM5

[Deprecated, refer to https://github.com/YangZhou1997/GEM5_DRAMSim2 for latest version] Simulating multi-core SmartNIC in GEM5

iuhhhisme/tensorflow-allreduce