SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 361–370 of 514 papers

TitleStatusHype
Biased Estimates of Advantages over Path Ensembles—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning—0
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space—0
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control—0
Clustered Reinforcement Learning—0
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Show:102550
← PrevPage 37 of 52Next →

No leaderboard results yet.