Curriculum learning and imitation learning have been leveraged extensively in
the robotics domain. However, minimal research has been done on leveraging
these ideas on control tasks over highly stochastic time-series data. Here, we
theoretically and empirically explore these approaches in a representative
control task over complex time-series data. We implement the fundamental ideas
of curriculum learning via data augmentation, while imitation learning is
implemented via policy distillation from an oracle. Our findings reveal that
curriculum learning should be considered a novel direction in improving
control-task performance over complex time-series. Our ample random-seed
out-sample empirics and ablation studies are highly encouraging for curriculum
learning for time-series control. These findings are especially encouraging as
we tune all overlapping hyperparameters on the baseline -- giving an advantage
to the baseline. On the other hand, we find that imitation learning should be
used with caution.Comment: AAAI 2024 AI4TS Workshop Ora