A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents

We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence.

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

This is a pytorch based implementation for our paper on learning to control self-assembling agents using deep reinforcement learning.

This repo covers the implementation for CMC (as well as Momentum Contrast and Instance Discrimination), which learns representations from multiview data in a self-supervised way (by multiview, we mean multiple sensory, multiple modal data, or literally multiple viewpoint data.

Estimating individual treatment effect: generalization bounds and algorithms.

Matlab and Python toolbox for fast Total Variation proximity operators.

This repository provides experiments, scripts, and instructions for reproducing the experiments in our paper, Low-Latency Graph Streaming Using Compressed Purely-Functional Trees. Our paper introduces Aspen, a graph-streaming system based on compressed purely-functional trees. Aspen is designed for maintaining a dynamic graph subject to updates by a single writer, while supporting multiple concurrent readers. Due to the fact that the graph is purely-functional, all operations in Aspen are strictly serializable.

The Coq development builds binary compilers that generate code using some implementation strategy. The parameters (modulus, hardware multiplication input bitwidth, etc.) are are specified on the command line of the compiler. The generated C code is written to standard output.

This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing dataset.

ADE20K is the largest open source dataset for semantic segmentation and scene parsing, released by MIT Computer Vision team. Follow the link below to find the repository for our dataset and implementations on Caffe and Torch7: https://github.com/CSAILVision/sceneparsing