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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 461–470 of 514 papers

TitleStatusHype
A Web-scale system for scientific knowledge exploration—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Exploration by Distributional Reinforcement Learning—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Variance Networks: When Expectation Does Not Meet Your ExpectationsCode0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
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