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
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments—0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects—0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Bag of Policies for Distributional Deep Exploration—0
Towards A Unified Agent with Foundation Models—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
Approximate information for efficient exploration-exploitation strategies—0
Maximum State Entropy Exploration using Predecessor and Successor Representations—0
DISCO-10M: A Large-Scale Music Dataset—0
Show:102550
← PrevPage 23 of 52Next →

No leaderboard results yet.