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Found 5 papers, 1 papers with code
MOORe: Model-based Offline-to-Online Reinforcement Learning
With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online.
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
Towards robust and domain agnostic reinforcement learning competitions
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.
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
Ranked #6 on
Multi-Person Pose Estimation
on OCHuman
