A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | |
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1 | Neuroscience-Inspired Artificial Intelligence (Hassabis overview article and outlook onto Deep Learning) | |||||||||||||||||||||||
2 | SIPB Deep Learning Group - code implementations | |||||||||||||||||||||||
3 | ||||||||||||||||||||||||
4 | yellow cell = assigned paper | |||||||||||||||||||||||
5 | green cell = free to select paper (recommended) | |||||||||||||||||||||||
6 | white cell = free to select paper | |||||||||||||||||||||||
7 | ||||||||||||||||||||||||
8 | ROBOTS BOOTSTRAPPED THROUGH LEARNING FROM EXPERIENCE (>200 papers) | |||||||||||||||||||||||
9 | SFM-Visual-SLAM (a number of different implementations) | |||||||||||||||||||||||
10 | ||||||||||||||||||||||||
11 | 392247 ISY Project: Deep Learning Architectures for AI (Pj) (SoSe 2019) | |||||||||||||||||||||||
12 | Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning | https://www.youtube.com/watch?v=B2s85xfo2uE | ||||||||||||||||||||||
13 | Deep, Probabilistic and Semantic 3D Reconstruction | https://github.com/paschalidoud/raynet | ||||||||||||||||||||||
14 | Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids | |||||||||||||||||||||||
15 | MOTS: Multi-Object Tracking and Segmentation | https://github.com/VisualComputingInstitute/mots_tools | ||||||||||||||||||||||
16 | Deep Marching Cubes: Learning Explicit Surface Representations | https://www.youtube.com/watch?v=vhrvl9qOSKM | ||||||||||||||||||||||
17 | UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning | https://github.com/drmaj/UnDeepVO | ||||||||||||||||||||||
18 | SfM-Net: Learning of Structure and Motion from Video | https://github.com/waxz/sfm_net | ||||||||||||||||||||||
19 | DeMoN: Depth and Motion Network for Learning Monocular Stereo | https://github.com/lmb-freiburg/demon | ||||||||||||||||||||||
20 | Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer | https://vision.in.tum.de/research/robotvision/replanning | https://github.com/VladyslavUsenko/ewok | |||||||||||||||||||||
21 | OctoMap: an efficient probabilistic 3D mapping framework based on octrees | https://octomap.github.io | ||||||||||||||||||||||
22 | Learning monocular visual odometry with dense 3D mapping from dense 3D flow | https://youtu.be/Ccj1O7yndIk?t=80 | ||||||||||||||||||||||
23 | Deep Auxiliary Learning for Visual Localization and Odometry | |||||||||||||||||||||||
24 | FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks | https://github.com/search?q=FlowNet+2.0 | https://youtu.be/JSzUdVBmQP4 | |||||||||||||||||||||
25 | Goal directed dynamics | |||||||||||||||||||||||
26 | Exploration by Random Network Distillation | https://github.com/openai/random-network-distillation | https://openai.com/blog/reinforcement-learning-with-prediction-based-rewards/ | |||||||||||||||||||||
27 | Deep Q-learning from Demonstrations | Roman Heinrich | https://ai.google/research/pubs/pub46980 | https://github.com/go2sea/DQfD | https://github.com/search?q=Deep+Q-Learning+from+demonstrations&type=Repositories | |||||||||||||||||||
28 | Deep Reinforcement Learning that Matters | |||||||||||||||||||||||
29 | Learning Latent Dynamics for Planning from Pixels | https://www.shortscience.org/paper?bibtexKey=journals/corr/1811.04551#wassname | http://www.arxiv-sanity.com/1811.04551 | |||||||||||||||||||||
30 | Adam: A method for stochastic optimization | Roman Heinrich | ||||||||||||||||||||||
31 | The option-critic architecture | |||||||||||||||||||||||
32 | Interpretable Latent Spaces for Learning from Demonstration | |||||||||||||||||||||||
33 | Conditional Affordance Learning for Driving in Urban Environments | http://www.cvlibs.net/publications/Sauer2018CORL_supplementary.pdf | http://www.youtube.com/watch?v=UtUbpigMgr0 | |||||||||||||||||||||
34 | ||||||||||||||||||||||||
35 | ||||||||||||||||||||||||
36 | TOPIC: NIPS 2018 | |||||||||||||||||||||||
37 | Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning | |||||||||||||||||||||||
38 | Artificial Intelligence for Prosthetics — challenge solutions (NeurIPS 2018) | https://www.crowdai.org/challenges/neurips-2018-ai-for-prosthetics-challenge | ||||||||||||||||||||||
39 | Recurrent World Models Facilitate Policy Evolution | |||||||||||||||||||||||
40 | ||||||||||||||||||||||||
41 | ||||||||||||||||||||||||
42 | TOPIC: DRL WS2018 | |||||||||||||||||||||||
43 | Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning | |||||||||||||||||||||||
44 | Towards Generalization and Simplicity in Continuous Control | |||||||||||||||||||||||
45 | PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations | |||||||||||||||||||||||
46 | MuJoCo: A physics engine for model-based control | |||||||||||||||||||||||
47 | ||||||||||||||||||||||||
48 | TOPIC 1: discrete action space - Atari games | |||||||||||||||||||||||
49 | Imagination-Augmented Agents for Deep Reinforcement Learning | Lennart Bramlage | https://github.com/yilundu/imagination_augmented_agents | |||||||||||||||||||||
50 | Reinforcement Learning with Unsupervised Auxiliary Tasks | Hendric Voss | https://youtu.be/-YiMVR3HEuY | https://github.com/miyosuda/unreal | https://github.com/NoobFang/multi-process-UNREAL | |||||||||||||||||||
51 | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning | Lukas Hindemith (AML) | https://github.com/oxwhirl/treeqn/ | |||||||||||||||||||||
52 | Curiosity-driven Exploration by Self-supervised Prediction | Sunkara Bhargav | https://pathak22.github.io/noreward-rl | https://youtu.be/_Z9ZP1eiKsI | ||||||||||||||||||||
53 | Progressive Neural Networks | Federico Rossetto | https://github.com/synpon/prog_nn | https://github.com/howland/DQN_PNN | ||||||||||||||||||||
54 | Model-Free Episodic Control | Aleksandrs Stier | https://github.com/sudeepraja/Model-Free-Episodic-Control | https://github.com/ShibiHe/Model-Free-Episodic-Control | ||||||||||||||||||||
55 | Dueling Network Architectures for Deep Reinforcement Learning | Andrew | https://youtu.be/Ma1b6EeHlV0 | https://github.com/gokhanettin/dddqn-tf | https://www.youtube.com/results?search_query=Dueling+Network+Architectures+for+Deep+Reinforcement+Learning&page=&utm_source=opensearch | |||||||||||||||||||
56 | Metacontrol for adaptive imagination-based optimization | https://github.com/deepmind/spaceship_dataset | ||||||||||||||||||||||
57 | Discovering objects and their relations from entangled scene representations | |||||||||||||||||||||||
58 | Action-Conditional Video Prediction Using Deep Networks in Atari Games | Simon Müller-Cleve | ||||||||||||||||||||||
59 | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain | |||||||||||||||||||||||
60 | Neural Episodic Control | |||||||||||||||||||||||
61 | Variational Intrinsic Control | ICLR17W | ||||||||||||||||||||||
62 | Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation | Federico Rossetto 190117 | https://github.com/mrkulk/hierarchical-deep-RL | http://mrkulk.github.io/notes/deephrl | https://www.reddit.com/r/MachineLearning/comments/4frm32/160406057_hierarchical_deep_reinforcement/ | |||||||||||||||||||
63 | Deep Reinforcement Learning Discovers Internal Models | |||||||||||||||||||||||
64 | The Reactor: A sample-efficient actor-critic architecture | |||||||||||||||||||||||
65 | Deep successor reinforcement learning | https://github.com/Ardavans/DSR | ||||||||||||||||||||||
66 | The predictron: End-to-end learning and planning | |||||||||||||||||||||||
67 | The Option-Critic Architecture | |||||||||||||||||||||||
68 | Reward Estimation for Variance Reduction in Deep Reinforcement Learning | |||||||||||||||||||||||
69 | FeUdal Networks for Hierarchical Reinforcement Learning | Lennart Bramlage 190117 | https://github.com/dmakian/feudal_networks | |||||||||||||||||||||
70 | Value Prediction Network | NIPS 2017 | ||||||||||||||||||||||
71 | Interaction Networks for Learning about Objects, Relations and Physics | https://github.com/jaesik817/Interaction-networks_tensorflow | ||||||||||||||||||||||
72 | Strategic Attentive Writer for Learning Macro-Actions | |||||||||||||||||||||||
73 | Deep Exploration via Bootstrapped DQN | https://www.youtube.com/watch?v=6SAdmG3zAMg&t=0s&index=1&list=PLdy8eRAW78uLDPNo1jRv8jdTx7aup1ujM | ||||||||||||||||||||||
74 | Independently Controllable Features | |||||||||||||||||||||||
75 | Asynchronous Methods for Deep Reinforcement Learning | Timo Weike | ||||||||||||||||||||||
76 | Behavior is Everything – Towards Representing Concepts with Sensorimotor Contingencies | https://github.com/vicariousinc/pixelworld | ||||||||||||||||||||||
77 | Visual Analogies between Atari Games for Studying Transfer Learning in RL | ICLR18W - Rejected | ||||||||||||||||||||||
78 | Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics | NO CODE IMPLEMENTATION | https://github.com/vicariousinc/schema-games | |||||||||||||||||||||
79 | ||||||||||||||||||||||||
80 | ||||||||||||||||||||||||
81 | TOPIC 2: high-dimensional continuous control problems - Locomotion | |||||||||||||||||||||||
82 | Proximal Policy Optimization Algorithms | Hendrik Lücking | ||||||||||||||||||||||
83 | High-Dimensional Continuous Control Using Generalized Advantage Estimation | Balavivek Sivanantham | https://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 | ||||||||||||||||
84 | Continuous deep q-learning with model-based acceleration | Sebastian Mueller | https://github.com/carpedm20/NAF-tensorflow | https://github.com/semueller/NAF-tensorflow | ||||||||||||||||||||
85 | Learning Robust Rewards with Adversarial Inverse Reinforcement Learning | Markus Vieth | ||||||||||||||||||||||
86 | Meta learning shared hierarchies | Jan Ebert | https://github.com/openai/mlsh | |||||||||||||||||||||
87 | Continuous control with deep reinforcement learning | Aleksandrs Stier (robotic-arms) | https://github.com/openai/baselines/tree/master/baselines/her | |||||||||||||||||||||
88 | DARLA: Improving Zero-Shot Transfer in Reinforcement Learning | Luca Lach | ||||||||||||||||||||||
89 | On the Continuity of Rotation Representations in Neural Networks | |||||||||||||||||||||||
90 | Divide-and-Conquer Reinforcement Learning | |||||||||||||||||||||||
91 | Emergence of Locomotion Behaviours in Rich Environments | https://youtu.be/hx_bgoTF7bs | ||||||||||||||||||||||
92 | Evolution Strategies as a Scalable Alternative to Reinforcement Learning | |||||||||||||||||||||||
93 | Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines | |||||||||||||||||||||||
94 | ||||||||||||||||||||||||
95 | ||||||||||||||||||||||||
96 | TOPIC 3: VAE | |||||||||||||||||||||||
97 | World Models | Sebastian | https://github.com/AppliedDataSciencePartners/WorldModels | |||||||||||||||||||||
98 | The Dreaming Variational Autoencoder for Reinforcement Learning Environments | Sebastian | ||||||||||||||||||||||
99 | Stable Reinforcement Learning with Autoencoders for Tactile and Visual Data | Simon Müller-Cleve | ||||||||||||||||||||||
100 | Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks |