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- W3197516770 abstract "Transferring reinforcement learning policies trained in a physical simulator to the real world is a highly challenging problem, because the gap between the simulation and reality, usually causes the transferred model to perform poorly in the real world. Many algorithms including domain randomization, have been proposed to try to bridge the gap between simulation and reality. However, most of them are to change the value of the corresponding data by superimposing gaussian noise on robot dynamics parameters or environmental data. Such policies often fail to solve the problem of long-term/intermittent missing data patterns caused by sensor failures in the actual operation of the robot. Faced with this problem, we proposed a memory-enhanced domain data pattern randomization method. This method achieves data enhancement by randomizing the distribution pattern of data connection, at the same time, the memory mechanism based on recurrent neural network is introduced into the decision model, to alleviate the jitter of environmental distribution caused by data pattern changes, so as to improve the decision-making ability of the robot in some observable scenes triggered by the change of data pattern." @default.
- W3197516770 created "2021-09-13" @default.
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- W3197516770 date "2021-03-05" @default.
- W3197516770 modified "2023-10-16" @default.
- W3197516770 title "A Domain Data Pattern Randomization based Deep Reinforcement Learning method for Sim-to-Real transfer" @default.
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- W3197516770 doi "https://doi.org/10.1145/3461353.3461373" @default.
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