3k citations · 3.2k across the 3 of their papers we have counts for
7 papers
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
Scaling Laws for Autoregressive Generative Modeling
Tom Henighan, Jared Kaplan, Mor Katz +16
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal imagetext models, and mathemat…
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typicall…
Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, :, Christopher Berner +24
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as lo…
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learnin…
Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse +2
In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for bo…