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 7180 of 514 papers

TitleStatusHype
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
Bootstrapped Meta-LearningCode0
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree SearchCode0
Generalization and Exploration via Randomized Value FunctionsCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
Better Exploration with Optimistic Actor CriticCode0
Scalable Exploration via Ensemble++Code0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement LearningCode0
Show:102550
← PrevPage 8 of 52Next →

No leaderboard results yet.