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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 481–490 of 514 papers

TitleStatusHype
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for MCMC—0
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning—0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches—0
Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences—0
Noisy Networks for ExplorationCode0
Count-Based Exploration in Feature Space for Reinforcement LearningCode0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
K-Means Clustering using Tabu Search with Quantized Means—0
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