4 citations · 7 across the 5 of their papers we have counts for
5 papers
Automated Rewards via LLM-Generated Progress Functions
Vishnu Sarukkai, Brennan Shacklett, Zander Majercik +3
Large Language Models (LLMs) have the potential to automate reward engineering by leveraging their broad domain knowledge across various tasks. However, they often need many iterat…
Learning to Move Like Professional Counter-Strike Players
David Durst, Feng Xie, Vishnu Sarukkai +9
In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the co…
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse +2
Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners.…
Megaverse: Simulating Embodied Agents at One Million Experiences per Second
Aleksei Petrenko, Erik Wijmans, Brennan Shacklett +1
We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of our engine enables physics-based simulation with hig…
Large Batch Simulation for Deep Reinforcement Learning
Brennan Shacklett, Erik Wijmans, Aleksei Petrenko +4
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of ove…