55 citations · 119 across the 9 of their papers we have counts for
5 papers · 1 filter
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…
EREBA: Black-box Energy Testing of Adaptive Neural Networks
Mirazul Haque, Yaswanth Yadlapalli, Wei Yang +1
Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determini…
Learning Diverse Policies in MOBA Games via Macro-Goals
Yiming Gao, Bei Shi, Xueying Du +10
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Ev…
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Hua Wei, Deheng Ye, Zhao Liu +5
Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) gen…
Sequenced-Replacement Sampling for Deep Learning
Chiu Man Ho, Dae Hoon Park, Wei Yang +1
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-bat…