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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 481490 of 514 papers

TitleStatusHype
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement LearningCode0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Scalable Online Exploration via CoverabilityCode0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
Scalable Sampling for High Utility PatternsCode0
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem SolvingCode0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
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