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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 151–175 of 514 papers

TitleStatusHype
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Generalization and Exploration via Randomized Value FunctionsCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context VariablesCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
Exploring through Random Curiosity with General Value FunctionsCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
Exploratory State Representation LearningCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
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