End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks – We show that, based on a deep neural network (DNN) model, the Atari 2600-inspired video game Atari 2600 can be learnt from non-linear video clips. This study shows that Atari 2600 can produce a video that is non-linear in time compared to a video that contains any video clip. The learner then selects the shortest path to the next block of video to the Atari 2600. The Atari 2600-produced video contains the longest path to the next block of video and thus this process has been learnt to be non-linear.
In this paper, we propose a novel temporal reinforcement learning approach for supervised learning. We propose a unified framework to learn the temporal representations of objects in a natural hierarchy. This approach is based on deep learning and local search, and it jointly learns to learn temporal representations. Experiments show that the proposed framework leads to state-of-the-art performance on a variety of tasks. We also observe that the method is robust to a variety of biases, which are commonly encountered when looking at state-of-the-art deep learning systems. We believe that the proposed framework is of general interest to researchers who are trying to improve their temporal reinforcement learning systems.
A Kernelized Bayesian Nonparametric Approach to Predicting Daily Driving Patterns
The Evolution of the Human Linguistic Classification Model
End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks
Clustering and Ranking from Pairwise Comparisons over Hilbert Spaces
Deep Reinforcement Learning with Temporal Algorithm and Trace DistanceIn this paper, we propose a novel temporal reinforcement learning approach for supervised learning. We propose a unified framework to learn the temporal representations of objects in a natural hierarchy. This approach is based on deep learning and local search, and it jointly learns to learn temporal representations. Experiments show that the proposed framework leads to state-of-the-art performance on a variety of tasks. We also observe that the method is robust to a variety of biases, which are commonly encountered when looking at state-of-the-art deep learning systems. We believe that the proposed framework is of general interest to researchers who are trying to improve their temporal reinforcement learning systems.