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 101110 of 514 papers

TitleStatusHype
Instance Temperature Knowledge DistillationCode0
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree SearchCode0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience0
Exploration by Learning Diverse Skills through Successor State Measures0
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown EnvironmentsCode0
World Models with Hints of Large Language Models for Goal Achieving0
Robust quantum dots charge autotuning using neural network uncertaintyCode0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability ObjectivesCode0
Show:102550
← PrevPage 11 of 52Next →

No leaderboard results yet.