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robo-gym – An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots
[article]
2020
arXiv
pre-print
Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real
arXiv:2007.02753v2
fatcat:a5lvnmnivffqnoizvubu436y3a