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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 321–330 of 514 papers

TitleStatusHype
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning—0
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation—0
Volumetric Spanners: an Efficient Exploration Basis for Learning—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
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