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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
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Deep Learning based Uncertainty Decomposition for Real-time Control—0
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
SEMI: Self-supervised Exploration via Multisensory Incongruity—0
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
End-Effect Exploration Drive for Effective Motor Learning—0
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