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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
TLDR
h-DQN is presented, a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning, and allows for flexible goal specifications, such as functions over entities and relations.
Deep Convolutional Inverse Graphics Network
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene
Language Understanding for Text-based Games using Deep Reinforcement Learning
TLDR
This paper employs a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback to map text descriptions into vector representations that capture the semantics of the game states.
Deep Successor Reinforcement Learning
TLDR
DSR is presented, which generalizes Successor Representations within an end-to-end deep reinforcement learning framework and has several appealing properties including: increased sensitivity to distal reward changes due to factorization of reward and world dynamics, and the ability to extract bottleneck states given successor maps trained under a random policy.
Synthesizing Programs for Images using Reinforced Adversarial Learning
TLDR
SPIRAL is an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images, and a surprising finding is that using the discriminator's output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering.
Unsupervised Learning of Object Keypoints for Perception and Control
TLDR
Transporter is introduced, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates that helps track objects and object parts across long time-horizons more accurately than recent similar methods.
Unsupervised Control Through Non-Parametric Discriminative Rewards
TLDR
An unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions, which leads to a co-operative game and a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations.
Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks
TLDR
This work takes an alternative approach to the problem of learning generative models of 3D shapes: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce3D shapes from these images.
Self-Supervised Intrinsic Image Decomposition
TLDR
This paper proposes a model that joins together an image decomposition pipeline, which predicts reflectance, shape, and lighting conditions given a single image, with a recombination function, a learned shading model used to recompose the original input based off of intrinsic image predictions, and a network that can use unsupervised reconstruction error as an additional signal to improve its intermediate representations.
Answering Range Queries Under Local Differential Privacy
TLDR
This work studies the problem of answering 1-dimensional range count queries under the constraint of LDP, a framework of differential privacy for privacy-preserving data analysis.
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