Stars
Graduate VLSI I project. L2 Ethernet Network Switch.
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Self-hardening firewall for large language models
Galois: C++ library for multi-core and multi-node parallelization
A more maintainable, easier to share version of the infamous http://mindprod.com/jgloss/unmain.html
Out-Of-Order Superscalar processor for the ARM instruction set
A customizable hardware prefetching framework using online reinforcement learning as described in the MICRO 2021 paper by Bera et al. (https://arxiv.org/pdf/2109.12021.pdf).
The financial transactions database designed for mission critical safety and performance.
Accelerate your web app development | Build fast. Run fast.
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement …
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Code exploring the use of reward machines in the context of cooperative multi-agent reinforcement learning.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Command-line program to download videos from YouTube.com and other video sites
A toolkit for developing and comparing reinforcement learning algorithms.
PyTorch implementation of soft actor critic
PyTorch implementations of deep reinforcement learning algorithms and environments
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
A set of Deep Reinforcement Learning Agents implemented in Tensorflow.
Lightweight and customizable notification daemon
Physoly website and submission portal for online contests.
A fast PostgreSQL Database Client Library for Python/asyncio.
FlatBuffers: Memory Efficient Serialization Library
A sophisticated vision pipeline to detect skystone vision targets in the presence of lots of noise. Focuses on versatility, not speed.