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 391–400 of 514 papers

TitleStatusHype
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Multi-Path Policy Optimization—0
MAME : Model-Agnostic Meta-Exploration—0
Structured exploration in the finite horizon linear quadratic dual control problem—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
Better Exploration with Optimistic Actor-Critic—0
Learning Transferable Graph Exploration—0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Show:102550
← PrevPage 40 of 52Next →

No leaderboard results yet.