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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
The Eigenoption-Critic Framework—0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
The Role of Coverage in Online Reinforcement Learning—0
The University of Cambridge Russian-English System at WMT13—0
Thompson Sampling Algorithms for Cascading Bandits—0
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments—0
Towards A Unified Agent with Foundation Models—0
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand—0
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