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UKAEA-RACE-CP(23)092023

"{Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place"
When transferring a Deep Reinforcement Learning (DRL) model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that al…
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UKAEA-RACE-CP(23)062023

"Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach"
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
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