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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 331–340 of 514 papers

TitleStatusHype
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
Data-Efficient Exploration with Self Play for Atari—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Principled Exploration via Optimistic Bootstrapping and Backward InductionCode0
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores—0
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement LearningCode0
Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifoldsCode0
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