Junhyuk Oh

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Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future image-frames depend on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are(More)
We propose a novel weakly-supervised semantic segmen-tation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches , our algorithm exploits auxiliary segmentation annotations available for different categories to guide seg-mentations on images with only image-level class labels. To make segmentation(More)
The network architectures of the proposed models and the baselines are illustrated in Figure 1. The weight of LSTM is initialized from a uniform distribution of [−0.08, 0.08]. The weight of the fully-connected layer from the encoded feature to the factored layer and from the action to the factored layer are initialized from a uniform distribution of [−1, 1](More)
In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existing deep reinforcement learning (DRL) architec-tures with our new memory-based DRL architec-tures. These tasks are designed to emphasize, in a controllable manner, issues that pose(More)
For all architectures, the first convolution layer consists of 32, 4 × 4, filters with a stride of 2 and a padding of 1. The second convolution layer consists of 64, 4 × 4, filters with a stride of 2 and a padding of 1. In Deep Q-Learning, batch size of 32 and discount factor of 0.99 are used. We used a replay memory size of 10 6 for random mazes and 5 × 10(More)
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