Reinforcement Learning on Slow Features of High-Dimensional Input Streams
نویسندگان
چکیده
منابع مشابه
Reinforcement Learning on Slow Features of High-Dimensional Input Streams
Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only appl...
متن کاملIncremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams
Slow Feature Analysis (SFA) extracts features representing the underlying causes of changes within a temporally coherent high-dimensional raw sensory input signal. Our novel incremental version of SFA (IncSFA) combines incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, IncSFA adapts along with non-stationary environments, is amenable to epi...
متن کاملIncremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams
We introduce here an incremental version of slow feature analysis (IncSFA), combining candid covariance-free incremental principal components analysis (CCIPCA) and covariance-free incremental minor components analysis (CIMCA). IncSFA's feature updating complexity is linear with respect to the input dimensionality, while batch SFA's (BSFA) updating complexity is cubic. IncSFA does not need to st...
متن کاملthe impact of computer-assisted language learning on achievement motivation of high school students
چکیده انگیزه دلیل اصلی رفتارهای ما است. به نظر می رسد انگیزه جزء جدایی ناپذیر فرایند یادگیری باشد. ارزش ذاتی موفقیت تمایل به پیشرفت را در یادگیرنده ایجاد میکند. به عبارت ساده این تمایل انگیزه پیشرفت نامیده میشود. انگیزه پیشرفت را میتوان در احساس یادگیرنده هنگام چالش با درس های مدرسه، لذت انجام فعالیت درسی، یا حس کشف پاسخ مشاهده کرد.حتی ممکن است انگیزه پیشرفت را در تلاش یادگیرنده برای جلب تایید...
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a “fast” reinforcement learning algorithm, we p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS Computational Biology
سال: 2010
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1000894