17 citations · 48 across the 12 of their papers we have counts for
4 papers · 1 filter
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
Sumeet Batra, Zhehui Huang, Aleksei Petrenko +3
We demonstrate the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement learning.…
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…
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems
Shashank Hegde, Anssi Kanervisto, Aleksei Petrenko
Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents…
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…