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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 121–130 of 514 papers

TitleStatusHype
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows—0
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization ApproachCode7
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Cognitive Planning for Object Goal Navigation using Generative AI Models—0
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
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