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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 191–200 of 514 papers

TitleStatusHype
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
Tuning Legged Locomotion Controllers via Safe Bayesian OptimizationCode1
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
Magnitude Attention-based Dynamic Pruning—0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Large-Batch, Iteration-Efficient Neural Bayesian Design OptimizationCode0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
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