87 citations · 170 across the 26 of their papers we have counts for
6 papers · 1 filter
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…
The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang +10
The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are n…
Distilling Reinforcement Learning Tricks for Video Games
Anssi Kanervisto, Christian Scheller, Yanick Schraner +1
Reinforcement learning (RL) research focuses on general solutions that can be applied across different domains. This results in methods that RL practitioners can use in almost any…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Multi-task Learning with Attention for End-to-end Autonomous Driving
Keishi Ishihara, Anssi Kanervisto, Jun Miura +1
Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approache…
Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search
Dylan Ashley, Anssi Kanervisto, Brendan Bennett
We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nas…