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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 361–370 of 514 papers

TitleStatusHype
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions—0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Implicit Generative Modeling for Efficient Exploration—0
Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining—0
Incentivizing Exploration with Selective Data Disclosure—0
Inferring Hierarchical Structure in Multi-Room Maze Environments—0
Information Content Exploration—0
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
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