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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 471–480 of 514 papers

TitleStatusHype
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown EnvironmentsCode0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
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