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 271–280 of 514 papers

TitleStatusHype
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Exploring through Random Curiosity with General Value FunctionsCode0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
Deep Active Ensemble Sampling For Image Classification—0
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
The Role of Coverage in Online Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
Show:102550
← PrevPage 28 of 52Next →

No leaderboard results yet.