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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 311–320 of 514 papers

TitleStatusHype
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
MADE: Exploration via Maximizing Deviation from Explored RegionsCode1
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
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---Code1
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
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