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Member:sungbeanJo_paper [2021/03/04 18:03] sungbean |
Member:sungbeanJo_paper [2021/04/21 22:08] (current) sungbean |
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| - | We use L1 as loss function ℓ instead | + | get_config_param active timestamp_mode |
| - | of the mean squared error (MSE), as it is more correlated | + | TIME_FROM_INTERNAL_OSC |
| - | to driving performance [11]. As our NoCrash benchmark | + | get_config_param active multipurpose_io_mode |
| - | consists of complex realistic driving conditions in the presence | + | OUTPUT_OFF |
| - | of dynamic agents, we collect demonstrations from an | + | get_config_param active sync_pulse_in_polarity |
| - | expert game AI using privileged information to drive correctly | + | ACTIVE_LOW |
| - | (i.e. always respecting rules of the road and not crashing | + | get_config_param active nmea_in_polarity |
| - | into any obstacle). Robustness to heavy noise in the | + | ACTIVE_HIGH |
| - | demonstrations is beyond the scope of our work, as we aim | + | get_config_param active nmea_baud_rate |
| - | to explore limitations of behavior cloning methods in spite | + | BAUD_9600 |
| - | of good demonstrations. Finally, we pre-trained our perception | + | |
| - | backbone on ImageNet to reduce initialization variance | + | |
| - | and benefit from generic transfer learning, a standard practice | + | |
| - | in deep learning seldom explored for behavior cloning. | + | |
