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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 251–275 of 514 papers

TitleStatusHype
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
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP—0
QueryBuilder: Human-in-the-Loop Query Development for Information Retrieval—0
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification—0
Deep Learning based Uncertainty Decomposition for Real-time Control—0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration—0
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search—0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Reinforcement Learning in Reward-Mixing MDPs—0
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
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