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

Showing 121130 of 514 papers

TitleStatusHype
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem SolvingCode0
Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability GraphsCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
Concurrent Meta Reinforcement LearningCode0
Show:102550
← PrevPage 13 of 52Next →

No leaderboard results yet.