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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

Showing 431440 of 514 papers

TitleStatusHype
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Variance Networks: When Expectation Does Not Meet Your ExpectationsCode0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian OptimizationCode0
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
Noisy Natural Gradient as Variational InferenceCode0
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