In 2011, as part of The New 52 reboot, the numbering of Action Comics was reset at #1 and a "Volume 2" was launched. This practice was resurrected for the two year New Krypton storyline. Eventually, writers began containing their storylines to single titles again and the DC editors ceased providing the chapter markers on the covers. Every Superman comic book published from the mid to late 1990s (including Annuals and Specials) possessed a sequential chapter number on the cover. These continuity markers were informally known as the "triangle" numbers, and were recycled at the end of each publishing year. Spatial redundancy is exploited and the head in the scene is approximated by an ellipsoid.
In order to provide an easily accessible reading order for consumers, the DC editorial staff began using a secondary numbering system displayed on the cover of each issue. This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. With the return of a monthly publishing schedule, Superman stories began to follow a chapter-based continuity, with storylines moving from one title to the next. Its brief tenure as a weekly contributed to Action passing the older Detective Comics in issue count. With issue #643, Action Comics Weekly reverted to its original monthly publication, and the title was changed back to Action Comics.
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From issue #601– 642, Action Comics became a 48-page anthology series again, however this time issues were distributed on a weekly basis and the title was officially changed to Action Comics Weekly. It maintained this status until the celebrated 50th anniversary landmark 600th issue. Beginning in January of 1987, Action Comics became a Superman team-up book, following the format of the late comic title, DC Comics Presents. The editors of DC comics wanted to produce three regular ongoing Superman titles. In September of 1986, the title went on a four-month publishing hiatus, in order to prepare for the Post-Crisis Superman relaunch event.
In the mid-1980s, Action Comics endured several format changes. For a year in 19 the back-up story was home to the Legion of Super-Heroes. By the 1970s, Action Comics featured Superman exclusively with occasional eight-page back-up stories spotlighting various characters such as the Atom or Aquaman. By the 1950s, the popularity of Superman had reached such a zenith, that the title became informally regarded as a "Superman" title. Superman made his first appearance in June of 1938 in the title sequence story from Action Comics #1.įor the first several years of publication, Action Comics existed as an anthology series, featuring multiple twelve-page stories featuring a variety of characters ranging from Congo Bill to Zatara. In 2018, it became only the second American comic book in history (after the 1930s-1960s Dell Comics title Four Color) to publish a 1,000th issue.Īside from its venerable publication history, Action Comics is more famously known as the birthplace of Superman, the most well-known super-hero in the world.
Infinite Frontier Action Comics was the second longest running comic book series of all time, with only Detective Comics (Volume 1) having been published longer.As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning.
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