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 131–140 of 514 papers

TitleStatusHype
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Bag of Policies for Distributional Deep Exploration—0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Show:102550
← PrevPage 14 of 52Next →

No leaderboard results yet.