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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 451–460 of 514 papers

TitleStatusHype
Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II—0
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration—0
New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism—0
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision ProcessesCode0
Goal-oriented Trajectories for Efficient Exploration—0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
Efficient Gradient-Free Variational Inference using Policy SearchCode0
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse RewardsCode0
Scheduled Policy Optimization for Natural Language Communication with Intelligent AgentsCode0
Meta-Learning for Stochastic Gradient MCMCCode0
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