instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi cal notion of Value of Informationthe expected im provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer tainty about its current value estimates for states. In this paper we investigate ways to represent and rea son about this uncertainty in algorithms where the sys tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q values based on these. These distributions are used to compute a myopic approximation to the value of infor mation for each action and hence to select the action that best balances exploration and exploitation.
answer:
Model based Bayesian Exploration

question:
We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.
answer:
Collaborative Filtering with Recurrent Neural Networks

question:
Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.
answer:
The VGLC: The Video Game Level Corpus