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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

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Showing 161–170 of 514 papers

TitleStatusHype
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Exploration by Learning Diverse Skills through Successor State Measures—0
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown EnvironmentsCode0
World Models with Hints of Large Language Models for Goal Achieving—0
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
NeoRL: Efficient Exploration for Nonepisodic RL—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
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