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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 261–270 of 514 papers

TitleStatusHype
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Policy Mirror Descent Inherently Explores Action Space—0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
Processing Document Collections to Automatically Extract Linked Data: Semantic Storytelling Technologies for Smart Curation Workflows—0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches—0
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need—0
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning—0
Provably Efficient Exploration in Policy Optimization—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
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