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 251–260 of 514 papers

TitleStatusHype
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
GFlowNets for AI-Driven Scientific Discovery—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Show:102550
← PrevPage 26 of 52Next →

No leaderboard results yet.