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

The Arcade Learning Environment (ALE) is an evaluation platform that posesthe challenge of building AI agents with general competency across dozens ofAtari 2600 games. It supports a variety of different problem settings and ithas been receiving increasing attention from the scientific community, leadingto some high-profile success stories such as the much publicized DeepQ-Networks (DQN). In this article we take a big picture look at how the ALE isbeing used by the research community. We show how diverse the evaluationmethodologies in the ALE have become with time, and highlight some key concernswhen evaluating agents in the ALE. We use this discussion to present somemethodological best practices and provide new benchmark results using thesebest practices. To further the progress in the field, we introduce a newversion of the ALE that supports multiple game modes and provides a form ofstochasticity we call sticky actions. We conclude this big picture look byrevisiting challenges posed when the ALE was introduced, summarizing thestate-of-the-art in various problems and highlighting problems that remainopen.

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents