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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 251–260 of 514 papers

TitleStatusHype
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distributionCode0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
A Langevin-like Sampler for Discrete DistributionsCode1
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
On Preemption and Learning in Stochastic SchedulingCode0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
Personalized Algorithmic Recourse with Preference ElicitationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
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