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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 401–410 of 514 papers

TitleStatusHype
Better Exploration with Optimistic Actor-Critic—0
Learning Transferable Graph Exploration—0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Receding Horizon CuriosityCode0
Deep exploration by novelty-pursuit with maximum state entropy—0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search—0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
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