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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 301–325 of 514 papers

TitleStatusHype
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
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning—0
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation—0
Volumetric Spanners: an Efficient Exploration Basis for Learning—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
World Models with Hints of Large Language Models for Goal Achieving—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Worst-Case Regret Bounds for Exploration via Randomized Value Functions—0
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
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