Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past

Profil Gaya Belajar pada Mahasiswa Program Studi Bahasa Jepang

The purpose of this study was to determine the learning style profile of students of the STBA YAPARI-ABA Japanese Language Studies Program in Bandung. The method used in this study is a qualitative descriptive method with observation and questionnaires as data collection instruments. The number of respondents in this study were 100 Japanese language study program students from 4 levels of study who were selected by random sampling technique. The results of this study are the mapping of student learning styles from level 1 to level 4. Although the tendency for learning styles in each dimension can be observed, there are several dimensions with respondents without a tendency showing a dominant number. When viewed from the profile of each respondent's study level, the profile combination is different. Level 1 has a learning style profile sensing - visual - reflective - sequential - introverted - inductive, level 2 has a learning style profile sensing - visual - active - global - extroverted - inductive, while levels 3 and level 4 have the same learning style profile, namely sensing – visual – reflective – global – introverted – deductive. In conclusion, from this study, the learning style profiles of students of the STBA YAPARI-ABA Bandung Japanese Language Study Program obtained a sensing – visual – reflective – global – introverted – inductive pattern.   Keywords: Learning Style, Japanese Language Students, Profil

Inductive Visual Localisation: Factorised Training for Superior Generalisation

End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition. However, RNNs often struggle to generalise to sequences longer than the ones encountered during training. In this work, we propose to optimise neural networks explicitly for induction. The idea is to first decompose the problem in a sequence of inductive steps and then to explicitly train the RNN to reproduce such steps. Generalisation is achieved as the RNN is not allowed to learn an arbitrary internal state; instead, it is tasked with mimicking the evolution of a valid state. In particular, the state is restricted to a spatial memory map that tracks parts of the input image which have been accounted for in previous steps. The RNN is trained for single inductive steps, where it produces updates to the memory in addition to the desired output. We evaluate our method on two different visual recognition problems involving visual sequences: (1) text spotting, i.e. joint localisation and reading of text in images containing multiple lines (or a block) of text, and (2) sequential counting of objects in aerial images. We show that inductive training of recurrent models enhances their generalisation ability on challenging image datasets.Comment: In BMVC 2018 (spotlight