ISY article matrix
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Neuroscience-Inspired Artificial Intelligence (Hassabis overview article and outlook onto Deep Learning)
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SIPB Deep Learning Group - code implementations
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yellow cell = assigned paper
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green cell = free to select paper (recommended)
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white cell = free to select paper
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ROBOTS BOOTSTRAPPED THROUGH LEARNING FROM EXPERIENCE (>200 papers)
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SFM-Visual-SLAM (a number of different implementations)
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392247 ISY Project: Deep Learning Architectures for AI (Pj) (SoSe 2019)
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Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planninghttps://www.youtube.com/watch?v=B2s85xfo2uE
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Deep, Probabilistic and Semantic 3D Reconstructionhttps://github.com/paschalidoud/raynet
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Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids
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MOTS: Multi-Object Tracking and Segmentationhttps://github.com/VisualComputingInstitute/mots_tools
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Deep Marching Cubes: Learning Explicit Surface Representationshttps://www.youtube.com/watch?v=vhrvl9qOSKM
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UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learninghttps://github.com/drmaj/UnDeepVO
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SfM-Net: Learning of Structure and Motion from Videohttps://github.com/waxz/sfm_net
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DeMoN: Depth and Motion Network for Learning Monocular Stereohttps://github.com/lmb-freiburg/demon
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Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular bufferhttps://vision.in.tum.de/research/robotvision/replanninghttps://github.com/VladyslavUsenko/ewok
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OctoMap: an efficient probabilistic 3D mapping framework based on octreeshttps://octomap.github.io
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Learning monocular visual odometry with dense 3D mapping from dense 3D flowhttps://youtu.be/Ccj1O7yndIk?t=80
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Deep Auxiliary Learning for Visual Localization and Odometry
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkshttps://github.com/search?q=FlowNet+2.0https://youtu.be/JSzUdVBmQP4
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Goal directed dynamics
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Exploration by Random Network Distillationhttps://github.com/openai/random-network-distillation
https://openai.com/blog/reinforcement-learning-with-prediction-based-rewards/
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Deep Q-learning from DemonstrationsRoman Heinrichhttps://ai.google/research/pubs/pub46980https://github.com/go2sea/DQfD
https://github.com/search?q=Deep+Q-Learning+from+demonstrations&type=Repositories
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Deep Reinforcement Learning that Matters
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Learning Latent Dynamics for Planning from Pixelshttps://www.shortscience.org/paper?bibtexKey=journals/corr/1811.04551#wassnamehttp://www.arxiv-sanity.com/1811.04551
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Adam: A method for stochastic optimizationRoman Heinrich
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The option-critic architecture
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Interpretable Latent Spaces for Learning from Demonstration
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Conditional Affordance Learning for Driving in Urban Environmentshttp://www.cvlibs.net/publications/Sauer2018CORL_supplementary.pdf
http://www.youtube.com/watch?v=UtUbpigMgr0
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TOPIC: NIPS 2018
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Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
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Artificial Intelligence for Prosthetics — challenge solutions (NeurIPS 2018)https://www.crowdai.org/challenges/neurips-2018-ai-for-prosthetics-challenge
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Recurrent World Models Facilitate Policy Evolution
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TOPIC: DRL WS2018
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Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning
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Towards Generalization and Simplicity in Continuous Control
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PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
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MuJoCo: A physics engine for model-based control
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TOPIC 1: discrete action space - Atari games
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Imagination-Augmented Agents for Deep Reinforcement LearningLennart Bramlagehttps://github.com/yilundu/imagination_augmented_agents
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Reinforcement Learning with Unsupervised Auxiliary TasksHendric Vosshttps://youtu.be/-YiMVR3HEuYhttps://github.com/miyosuda/unreal
https://github.com/NoobFang/multi-process-UNREAL
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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement LearningLukas Hindemith (AML)https://github.com/oxwhirl/treeqn/
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Curiosity-driven Exploration by Self-supervised PredictionSunkara Bhargavhttps://pathak22.github.io/noreward-rl
https://youtu.be/_Z9ZP1eiKsI
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Progressive Neural NetworksFederico Rossettohttps://github.com/synpon/prog_nnhttps://github.com/howland/DQN_PNN
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Model-Free Episodic ControlAleksandrs Stierhttps://github.com/sudeepraja/Model-Free-Episodic-Control
https://github.com/ShibiHe/Model-Free-Episodic-Control
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Dueling Network Architectures for Deep Reinforcement LearningAndrewhttps://youtu.be/Ma1b6EeHlV0https://github.com/gokhanettin/dddqn-tf
https://www.youtube.com/results?search_query=Dueling+Network+Architectures+for+Deep+Reinforcement+Learning&page=&utm_source=opensearch
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Metacontrol for adaptive imagination-based optimizationhttps://github.com/deepmind/spaceship_dataset
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Discovering objects and their relations from entangled scene representations
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Action-Conditional Video Prediction Using Deep Networks in Atari GamesSimon Müller-Cleve
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Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
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Neural Episodic Control
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Variational Intrinsic Control ICLR17W
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivationFederico Rossetto 190117https://github.com/mrkulk/hierarchical-deep-RLhttp://mrkulk.github.io/notes/deephrl
https://www.reddit.com/r/MachineLearning/comments/4frm32/160406057_hierarchical_deep_reinforcement/
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Deep Reinforcement Learning Discovers Internal Models
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The Reactor: A sample-efficient actor-critic architecture
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Deep successor reinforcement learninghttps://github.com/Ardavans/DSR
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The predictron: End-to-end learning and planning
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The Option-Critic Architecture
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Reward Estimation for Variance Reduction in Deep Reinforcement Learning
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FeUdal Networks for Hierarchical Reinforcement LearningLennart Bramlage 190117https://github.com/dmakian/feudal_networks
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Value Prediction NetworkNIPS 2017
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Interaction Networks for Learning about Objects, Relations and Physicshttps://github.com/jaesik817/Interaction-networks_tensorflow
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Strategic Attentive Writer for Learning Macro-Actions
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Deep Exploration via Bootstrapped DQN
https://www.youtube.com/watch?v=6SAdmG3zAMg&t=0s&index=1&list=PLdy8eRAW78uLDPNo1jRv8jdTx7aup1ujM
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Independently Controllable Features
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Asynchronous Methods for Deep Reinforcement LearningTimo Weike
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Behavior is Everything – Towards Representing Concepts with Sensorimotor Contingencieshttps://github.com/vicariousinc/pixelworld
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Visual Analogies between Atari Games for Studying Transfer Learning in RLICLR18W - Rejected
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Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive PhysicsNO CODE IMPLEMENTATION
https://github.com/vicariousinc/schema-games
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TOPIC 2: high-dimensional continuous control problems - Locomotion
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Proximal Policy Optimization AlgorithmsHendrik Lücking
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High-Dimensional Continuous Control Using Generalized Advantage EstimationBalavivek Sivananthamhttps://github.com/Anjum48/rl-examples/tree/56aca982fcf4426c02aa7e5fb58a4f8affab8020
https://github.com/ray-project/ray/tree/master/python/ray/rllib
https://github.com/search?q=Generalized+Advantage+Estimation&ref=opensearch&type=Code
https://github.com/search?p=2&q=HIGH-DIMENSIONAL+CONTINUOUS+CONTROL+USING+GENERALIZED+ADVANTAGE+ESTIMATION&ref=opensearch&type=Code
https://www.youtube.com/results?search_query=Generalized+Advantage+Estimation&page=&utm_source=opensearch
https://www.youtube.com/watch?v=jymFj7bNsKg
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Continuous deep q-learning with model-based accelerationSebastian Muellerhttps://github.com/carpedm20/NAF-tensorflow https://github.com/semueller/NAF-tensorflow
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Learning Robust Rewards with Adversarial Inverse Reinforcement LearningMarkus Vieth
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Meta learning shared hierarchiesJan Eberthttps://github.com/openai/mlsh
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Continuous control with deep reinforcement learning
Aleksandrs Stier (robotic-arms)
https://github.com/openai/baselines/tree/master/baselines/her
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DARLA: Improving Zero-Shot Transfer in Reinforcement LearningLuca Lach
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On the Continuity of Rotation Representations in Neural Networks
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Divide-and-Conquer Reinforcement Learning
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Emergence of Locomotion Behaviours in Rich Environmentshttps://youtu.be/hx_bgoTF7bs
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
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Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
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TOPIC 3: VAE
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World ModelsSebastianhttps://github.com/AppliedDataSciencePartners/WorldModels
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The Dreaming Variational Autoencoder for Reinforcement Learning EnvironmentsSebastian
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Stable Reinforcement Learning with Autoencoders for Tactile and Visual DataSimon Müller-Cleve
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Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
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