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 401–410 of 514 papers

TitleStatusHype
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations—0
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores—0
DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems—0
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience—0
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Show:102550
← PrevPage 41 of 52Next →

No leaderboard results yet.