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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 331–340 of 514 papers

TitleStatusHype
ActiveGAMER: Active GAussian Mapping through Efficient Rendering—0
Active Model Estimation in Markov Decision Processes—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Adaptformer: Sequence models as adaptive iterative planners—0
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
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