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REM

Random Ensemble Mixture

Reinforcement LearningIntroduced 200048 papers

Description

Random Ensemble Mixture (REM) is an easy to implement extension of DQN inspired by Dropout. The key intuition behind REM is that if one has access to multiple estimates of Q-values, then a weighted combination of the Q-value estimates is also an estimate for Q-values. Accordingly, in each training step, REM randomly combines multiple Q-value estimates and uses this random combination for robust training.

Papers Using This Method

Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices2025-05-31 REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization2025-01-01 MobileNetV2: A lightweight classification model for home-based sleep apnea screening2024-12-28 ECG-SleepNet: Deep Learning-Based Comprehensive Sleep Stage Classification Using ECG Signals2024-12-02 ReferEverything: Towards Segmenting Everything We Can Speak of in Videos2024-10-30 Efficient and Private Marginal Reconstruction with Local Non-Negativity2024-10-01 Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications2024-10-01 Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations2024-06-16 Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models2024-06-06 Nonlinear Transformations Against Unlearnable Datasets2024-06-05 Spatial-temporal analysis of neural desynchronization in sleep-like states reveals critical dynamics2024-05-28 Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing2024-03-13 Interference Mitigation in LEO Constellations with Limited Radio Environment Information2024-02-19 Improving Token-Based World Models with Parallel Observation Prediction2024-02-08 Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data2024-01-16 Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling2024-01-08 Modeling non-linear Effects with Neural Networks in Relational Event Models2023-12-19 Wake-Sleep Consolidated Learning2023-12-06 Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes2023-11-10 SI-SD: Sleep Interpreter through awake-guided cross-subject Semantic Decoding2023-09-28