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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 91–100 of 514 papers

TitleStatusHype
CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning—0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings—0
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration—0
Reward-Centered ReST-MCTS: A Robust Decision-Making Framework for Robotic Manipulation in High Uncertainty EnvironmentsCode0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
A Transformer Model for Predicting Chemical Reaction Products from Generic Templates—0
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
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