OriginalStAdv Ours Figure 1: Comparison between the original image and adversarial images obtained by spatially transformations (StAdv) [39]and spatial chroma-shift (Ours). The proposed method significantly reduces the perceptible deformation while successfully fooling the target models.
Many everyday activities are sequential in nature. That is, they can be seen as a sequence of subactions and sometimes subgoals. In the motor execution of sequential action, context effects are observed in which later subactions modulate the execution of earlier subactions (e.g., reaching for an overturned mug, people will optimize their grasp to achieve a comfortable end state). A trajectory (movement) adaptation of an often‐used paradigm in the study of sequential action, the serial response time task, showed several context effects of which centering behavior is of special interest. Centering behavior refers to the tendency (or strategy) of subjects to move their arm or mouse cursor to a position equidistant to all stimuli in the absence of predictive information, thereby reducing movement time to all possible targets. In the current study, we investigated sequential action in a virtual robotic agent trained using proximal policy optimization, a state‐of‐the‐art deep reinforcement learning algorithm. The agent was trained to reach for appearing targets, similar to a serial response time task given to humans. We found that agents were more likely to develop centering behavior similar to human subjects after curricularized learning. In our curriculum, we first rewarded agents for reaching targets before introducing a penalty for energy expenditure. When the penalty was applied with no curriculum, many agents failed to learn the task due to a lack of action space exploration, resulting in high variability of agents' performance. Our findings suggest that in virtual agents, similar to infants, early energetic exploration can promote robust later learning. This may have the same effect as infants' curiosity‐based learning by which they shape their own curriculum. However, introducing new goals cannot wait too long, as there may be critical periods in development after which agents (as humans) cannot flexibly learn to incorporate new objectives. These lessons are making their way into machine learning and offer exciting new avenues for studying both human and machine learning of sequential action.
Video oyunu araştırması, karmaşık yöntemlerin ve algoritmaların geliştirildiği, sürekli değişmekte olan, dinamik bir alandır. Prosedürel içerik üretimi, kullanıcı tarafından oluşturulan parçaları video oyunu içeriğini otomatikleştirmek ve geliştirmek için algoritmalarla birleştirmeyi amaçlamakta ve bu yöntemlerin temelini oluşturmaktadır. Bununla birlikte, sonuçlar oyun mekaniğine ve oyunun oynanış biçimine değil, çoğunlukla oyun estetiğine yansımaktadır. Bu çalışmada, "tuval olarak oyun sahnesi" konsepti ile kullanıma hazır çarpıştırıcılar ve oyun estetiğini geliştiren, sanatsal açıdan farklı stiller kullanarak iki boyutlu oyun seviyesindeki bir görüntüyü basit bir prototip oyun geliştirme ortamına dönüştürebilen yöntem ve süreç sunulmaktadır. Bu amaçla, giriş oyun seviyesi görüntüsünün kenar ve renk bazlı özellikleri Canny kenar belirleme, basit doğrusal yinelemeli kümeleme ve Felzenszwalb segmentasyonu kullanılarak çıkarılmaktadır. Daha sonra, Unity oyun motoru, mekansal kontrol ile oyun seviyesinin stilinin aktarıldığı kenar ve renk özelliklerine göre çarpıştırıcılar oluşturmak için kullanılmaktadır. Farklı sinir stil transfer algoritmalarının sonuçları, Super Mario, Lode Runner ve Kid Icarus gibi oyunlar üzerinde karşılaştırılmakta ve tartışılmaktadır. Sonuçlar, bu çalışmanın oyun mekaniği ve oyun estetiğine odaklanarak iki boyutlu video oyunu geliştirmeyi kolaylaştırma potansiyeline sahip bir araç olduğunu göstermektedir.
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