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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 371–380 of 514 papers

TitleStatusHype
Task-agnostic Exploration in Reinforcement Learning—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Multirobot Coverage of Modular EnvironmentsCode0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
Bayesian optimisation of large-scale photonic reservoir computers—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised LearningCode0
Active Model Estimation in Markov Decision Processes—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
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