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DeepMind Lab

2016-12-12 · Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen

DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.

📄 PDF Abstract BibTeX arXiv:1612.03801

Code (5)

deepmind/lab

deepmind/labmaze

google-deepmind/lab

jkulhanek/dmhouse

jkulhanek/dmlab-vn

Tasks

BIG-bench Machine Learning

DeepMind Lab2D

2020-11-13 · Charles Beattie, Thomas Köppe, Edgar A. Duéñez-Guzmán, Joel Z. Leibo

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs o…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

DeepMind Control Suite

2018-01-02 · Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez 외

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are writte…

continuous-control Continuous Control MuJoCo reinforcement-learning+2

marl-jax: Multi-Agent Reinforcement Leaning Framework

2023-03-24 · Kinal Mehta, Anuj Mahajan, Pawan Kumar

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL …

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

2022-09-22 · Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand 외

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial en…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

2020-04-08 · Aravind Srinivas, Michael Laskin, Pieter Abbeel

We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted f…

Atari Games Atari Games 100k Continuous Control Contrastive Learning+3