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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

Showing 301310 of 514 papers

TitleStatusHype
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning0
Exploratory State Representation LearningCode0
Exploring More When It Needs in Deep Reinforcement Learning0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level0
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain0
Bootstrapped Meta-LearningCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
Learn2Hop: Learned Optimization on Rough Landscapes0
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