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Found 10 papers, 4 papers with code
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1, 600 submissions.
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions.
The Neural MMO Platform for Massively Multiagent Research
Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems.
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
Neural MMO: A massively multiplayer game environment for intelligent agents
We demonstrate how this platform can be used to study behavior and learning in large populations of neural agents.
GAN You Do the GAN GAN?
In this work, we answer one of deep learning's most pressing questions: GAN you do the GAN GAN?
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources.
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer
We present a novel Dynamic Differentiable Reasoning (DDR) framework for jointly learning branching programs and the functions composing them; this resolves a significant nondifferentiability inhibiting recent dynamic architectures.
Ranked #8 on
Visual Question Answering (VQA)
on CLEVR
Language Modeling with Recurrent Highway Hypernetworks
We present extensive experimental and theoretical support for the efficacy of recurrent highway networks (RHNs) and recurrent hypernetworks complimentary to the original works.
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures
We present a simple dynamic batching approach applicable to a large class of dynamic architectures that consistently yields speedups of over 10x.
