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awesome-real-world-rl
Great resources for making Reinforcement Learning work in Real Life situations. Papers,projects and more.
https://github.com/ugurkanates/awesome-real-world-rl
Last synced: 1 day ago
JSON representation
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Simulation to Real with GANs
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Multi Agent in Real World
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Projects
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Libraries
- State Representation Learning Zoo - Implements most of SRL algorithms in robotic settings.
- Robotics Reinforcement Learning - Customizable PyBullet gym robotic environment.
- RL Bench - Includes object oriented approaches for robotic RL tasks.
- RL Garage - Includes common RL real world robotic benchmarks.
- RL Robotic Meta World - Robotic Manipulation Tasks - Real world examples regarding to Meta Learning.
- GYM Sawyer Robot Environments - ROS - Sawyer robot with ROS and RL.
- RL Robotic Meta World - Robotic Manipulation Tasks - Real world examples regarding to Meta Learning.
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Papers
- Reinforcement Learning Applications
- Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
- Asymmetric Actor Critic for Image-Based Robot Learning
- Towards Learning Robots Which Can Adapt on the Fly
- Thinking while moving : Deep RL with Concurrent Control
- Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
- Quantifying the Reality Gap in Robotic Manipulation Tasks
- Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
- Sim-to-Real Robot Learning from Pixels with Progressive Nets
- A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
- Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
- Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
- Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
- The Ingredients of Real-World Robotic Reinforcement Learning
- Learning personalized treatments via IRL
- Reinforcement Learning Applications
- Challenges of Real-World Reinforcement Learning
- An empirical investigation of the challenges of real-world reinforcement learning
- Generalized State-Dependent Exploration for Deep Reinforcement Learning in Robotics
- Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
- Scaling simulation-to-real transfer by learning composable robot skills
- The Importance and the Limitations of Sim2Real for Robotic Manipulation in Precision Agriculture
- Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
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Conference Talks
- Reinforcement learning and it's growing role in AI with Real World examples - Microsoft 2020
- RL for Health Care Panel Discussion June 28 2020
- Learning to Dock Robustly
- BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
- An empirical investigation of the challenges of real world Reinforcement Learning
- GPU Technology Conference 2017: Sim2real collision avoidance for indoor navigation of mobile robots
- Reinforcement learning and it's growing role in AI with Real World examples - Microsoft 2020
- RL in Real World 2020 Conference
- RL for Real Life Panel Discussion June 27-28 2020
- RL for Health Care Panel Discussion June 28 2020
- Learning to Dock Robustly
- On Demand Ride Sharing with RL
- BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
- An empirical investigation of the challenges of real world Reinforcement Learning
- Why Sim2Real ? - July 10 2020
- Visual Learning and Reasoning for Robotic Manipulation
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Datasets
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Books
- Deep RL Hands On 2nd Edition Packt Edition - Has entire chapter dedicated to real world robotics agent.
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python - Few chapters related to real world applications.
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python - Few chapters related to real world applications.
- Deep RL Hands On 2nd Edition Packt Edition - Has entire chapter dedicated to real world robotics agent.
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Education
- Stanford CS330 Meta RL + Multi Task by Chelsea Finn 2019-2020
- CMU Real Life Reinforcement Learning 2015 - A lot of advanced topics as well as talks about DARPA RL Challenge.
- Reinforcement Learning Robot Course CMPUT 652 Fall 2019
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Meta Reinforcement Learning
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Imitation Learning
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Real World Examples
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Offline RL
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Prominent Researchers & Companies to Follow
- Sergey Levine - Google Robotics,UC Berkeley.
- Chelsea Finn - Stanford Uni,Google Brain.
- Ashvin Nair - UC Berkeley.
- 99andBeyond - Chemical Company Uses RL for Research,Biomedical & Chemistry.
- X Company - A lot of physics and real life based problems with RL.
- Damien Ernst - Real Life solutions to energy related problems.
- Xinyang Geng - A lot of Meta Learning and locomotion RL.
- Maxim Lapan - Author of Deep RL Hands-On book.
- Scott Fujimoto - One of the most prominent Offline RL researcher.
Categories
Sub Categories
Keywords
pytorch
3
multi-task
2
mujoco
2
meta-rl
2
benchmark-environments
2
reinforcement-learning
2
deep-learning
2
representation-learning
2
neural-network
1
inverse-model
1
forward-model
1
autoencoder
1
torch
1
pix2pix
1
image-manipulation
1
image-generation
1
generative-adversarial-network
1
gans
1
gan
1
cyclegan
1
computer-vision
1
computer-graphics
1
srl
1
state-representation-learning
1
vae
1
arm
1
baselines
1
baxter-robot
1
gym
1
kuka
1
pybullet
1
robotics
1
state
1
reproducibility
1
rl-algorithms
1
tensorflow
1