mlsanityData to 14 Oct 2026

Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series

arXiv 2311.13326 ↗First posted 22 Nov 2023cs.LG cs.AI q-fin.PMcited by 1

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 16 of its 37 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

16 of 37 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  5. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  6. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  7. 2019On The Power of Curriculum Learning in Training Deep Networkscited by 286
  8. 2023Goal-Conditioned Imitation Learning using Score-based Diffusion Policiescited by 236
  9. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 150
  10. 2021Recent Advances in Reinforcement Learning in Financecited by 110
  11. 2023Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrationscited by 75
  12. 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 67
4 more
  1. 2020Learning perturbation sets for robust machine learningcited by 48
  2. 2021Universal Trading for Order Execution with Oracle Policy Distillationcited by 30
  3. 2019Curriculum Learning in Deep Neural Networks for Financial Forecastingcited by 8
  4. 2020Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goalscited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Review of Data-centric Time Series Analysis from Sample, Feature, and Periodcited by 1

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  2. 2023Curriculum-Based Imitation of Versatile Skills
  3. 2022TaSIL: Taylor Series Imitation Learning
  4. 2023Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
  5. 2025FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
  6. 2025Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control
  7. 2024Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
  8. 2025Robust Offline Imitation Learning Through State-level Trajectory Stitching