BoT has the potential to surpass Llama3-70B model.

The process for calculating these metrics is described in Appendix C. Moreover, to ensure the comparability between prediction performance metrics and driving performance metrics in the radar plot, we normalize all metrics to the scale of [0, 1]. In the subsequent section, we provide an overview of the DESPOT planner. These two values can only be inferred from history. The safety is represented by the normalized collision rate.

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Acontinual learning agent learns online with anon-stationary andnever-ending stream ofdata. Thekeytosuch learning process istoovercome thecatastrophic forgetting of previously seen data, which is a well known problem of neuralnetworks.

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Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small memory buffer; subsequently, they repeatedly optimize on the latter to prevent catastrophic forgetting. This work draws attention to a hidden pitfallofthis widespread practice: repeated optimization onasmall pool of data inevitably leads to tight and unstable decision boundaries, which are a major hindrance to generalization.

Dayan's SR [3] is well-suited for transfer learning in settings with fixed dynamics, as the decomposition ofthevaluefunction intorepresentations ofexpected outcomes (future stateoccupancies) andcorresponding rewards allowsustoquickly recompute values under newrewardsettings.