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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 101–125 of 514 papers

TitleStatusHype
Efficient Exploration of Gradient Space for Online Learning to Rank—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep exploration by novelty-pursuit with maximum state entropy—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep Exploration via Randomized Value Functions—0
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network—0
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Efficient Pose and Cell Segmentation using Column Generation—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
DISCO-10M: A Large-Scale Music Dataset—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Discovering Context Specific Causal Relationships—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
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