English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Efficient exploration is one of the key challenges for reinforcement learning(RL) algorithms. Most traditional sample efficiency bounds require strategicexploration. Recently many deep RL algorithms with simple heuristic explorationstrategies that have few formal guarantees, achieve surprising success in manydomains. These results pose an important question about understanding theseexploration strategies such as $e$-greedy, as well as understanding whatcharacterize the difficulty of exploration in MDPs. In this work we proposeproblem specific sample complexity bounds of $Q$ learning with random walkexploration that rely on several structural properties. We also link ourtheoretical results to some empirical benchmark domains, to illustrate if ourbound gives polynomial sample complexity in these domains and how that isrelated with the empirical performance.

When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms