A Method for Animating Children’s Drawings of the Human Figure
Harrison Jesse Smith, Qingyuan Zheng, Yifei Li, Somya Jain, Jessica K. Hodgins
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
Deep Reinforcement Learning (Deep RL) Workshop at NeurIPS
Abstract
Prioritized Level Replay (PLR) has been shown to induce adaptive curricula that improve the sample-efficiency and generalization of reinforcement learning policies in environments featuring multiple tasks or levels. PLR selectively samples training levels weighed by a function of recent temporal-difference (TD) errors experienced on each level. We explore the dispersion of returns as an alternative prioritization criterion to address certain issues with TD error scores.
Latest Publications
TOG - 2023
Reasoning over Public and Private Data in Retrieval-Based Systems
Simran Arora, Patrick Lewis, Angela Fan, Jacob Kahn, Christopher Ré
Hard Disk Drive Failure Analysis and Prediction: An Industry View
Zach Miller, Olusiji Medaiyese, Madhavan Ravi, Alex Beatty, Fred Lin