[PDF] Top 20 Learning how to Active Learn: A Deep Reinforcement Learning Approach

... based Active Learning, a novel approach for learning a dynamic active learning strategy from ...learns how to actively select data, formalised as a reinforcement ... See full document

... Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems ...policy learning methods are also restricted to learn from ... See full document

... 3- Active learning to refine the initially learned ...supervised deep learning with data aggregation using active learning to produce a robust imitation learning ... See full document

... interactive learning formulation named APRIL (Active Pref- erence ReInforcement Learning), which is able to make structured predictions without referring to the gold standard ...an ... See full document

... Traditional active learning algorithms rely on various heuristics (Settles, 2010), such as un- certainty sampling (Settles and Craven, 2008; Houlsby et ...transfer learning, pre-existing labeled data ... See full document

... this approach is an encoder- decoder architecture implemented by recurrent neural ...a reinforcement learning framework (Williams, 1992): it explores the space of possible simplifications while ... See full document

... Table 1: Left Column: Dialogue simulation between two agents using a 4-layer LSTM encoder-decoder trained on the OpenSubtitles dataset. The first turn (index 1) is input by the authors. Then the two agents take turns ... See full document

... in deep reinforcement learning for swarms: the high and possibly changing dimensionality of information perceived by each ...that learning embeddings end-to-end using neural network features ... See full document

... makes learning more difficult. Observe how the training curve, around the step 40, 000 gets a sequence of very low rewards (around − ...to learn how to avoid obstacles and last longer, but end ... See full document

... self-reflective learning model that depends of deep combined actor-critic layered architecture has been ...the learning process for successful experience or forgetting it for bad ...the ... See full document

... We evaluate our model using two Multi-User Dungeon (MUD) games (Curtis, 1992; Amir and Doyle, 2002). The first game is designed to pro- vide a controlled setup for the task, while the sec- ond is a publicly available one ... See full document

... data-driven approach to train a model that can conduct evalu- ation in learning for paraphrasing ...Seq2Seq learning model with attention and copy mecha- nism (Bahdanau et ...a deep matching ... See full document

... A reinforcement learning algorithm is studied from 1000 training sessions, and then evaluates in the 200 non-learning stage, the performance of agent is measured with the mean score of the evaluation ... See full document

... a deep learning model to approximate a function from sample input- output ...general-purpose deep learning structure, since there are many different functions in robotics that researchers and ... See full document

... Reinforcement learning methods get an especially large amount of attention in educational interactive narrative adaptation, generally because the highly uncertain student- narrative planner interaction and ... See full document

... Authors of [12] combined PER with Double Q-learning and Snapshot En- sembling and tested their agent in VizDoom Defend The Center scenario. The authors train enemy detector and Q-function in a joint manner; this ... See full document

... of reinforcement learning algorithms that efficiently explore intractably large state spaces remains an important ...Model learning algorithms exploit gener- alization in an underlying model of the ... See full document

... for active Bayesian perception and reinforce- ment learning is tested with a simple but illustrative task of perceiving object curvature using tapping movements of a biomimetic fingertip with unknown ... See full document

... the cut-loop routine is applied. Prot-sharing uses trial and error experiences, and reinforces eective rules instead of estimating values for the dierent state. Therefore, it uses this policy to escape states susceptible ... See full document

... temporal-difference reinforcement learning algorithm which often explicitly stores state values using lookup ...as deep neural networks, to estimate state ...various deep neural networks and ... See full document