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 221–230 of 514 papers

TitleStatusHype
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Better Exploration with Optimistic Actor-Critic—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Show:102550
← PrevPage 23 of 52Next →

No leaderboard results yet.