Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationImitation LearningTime SeriesSimilar Papers 제목 키워드 기반
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especi…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for high-dimensional systems, but its relatively poor sample complexity often forces training in simulated environments. Even…
continuous-controlContinuous Controlreinforcement-learningReinforcement Learning+1Curriculum Learning in Deep Neural Networks for Financial Forecasting
For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time…
DecoderTime SeriesTime Series AnalysisTime Series Forecasting+1Discovering Self-Protective Falling Policy for Humanoid Robot via Deep Reinforcement Learning
Humanoid robots have received significant research interests and advancements in recent years. Despite many successes, due to their morphology, dynamics and limitation of control policy, humanoid robots are prone to fall…
Reinforcement LearningCurriculum Imitation Learning of Distributed Multi-Robot Policies
Learning control policies for multi-robot systems (MRS) remains a major challenge due to long-term coordination and the difficulty of obtaining realistic training data. In this work, we address both limitations within an…