Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

no code implementations • 29 Mar 2021 • Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, Linas Klimas, Matthew Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.

reinforcement-learning Reinforcement Learning (RL)

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

no code implementations • 19 Jul 2020 • João Schapke, Anderson Tavares, Mariana Recamonde-Mendoza

The identification of essential genes/proteins is a critical step towards a better understanding of human biology and pathology.

Graph Attention