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 181–190 of 514 papers

TitleStatusHype
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Scalable Online Exploration via CoverabilityCode0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
Noisy Spiking Actor Network for Exploration—0
Show:102550
← PrevPage 19 of 52Next →

No leaderboard results yet.