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Learning To Simulate

Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker

arXiv:1810.02513v2cs.LGcs.CVstat.ML

TL;DR

When data is costly to annotate or hard to acquire, it remains unclear what distribution a simulator should use to generate training data. The paper uses policy gradients to adjust simulator parameters for a model’s validation performance, and reports that the approach can quickly find competitive parameters in controlled experiments and computer-vision applications.

  • Problem

    Training data can be costly to annotate or difficult to acquire, and the distribution for synthesizing simulator data remains an open question.

    Source: §1 · p. 1

  • Method

    A policy uses policy gradients to adjust parameters of a simulator, generating synthetic datasets to train a model whose validation performance supplies the reward.

    Source: §1 · p. 2, §2.2 · p. 2

  • Results

    Experiments on toy data and computer-vision tasks find good simulator parameters that compete with and sometimes outperform validation-set parameters.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The experiments demonstrate the capability of learning to simulate on synthetic and real data, with synthetic data serving as a practical avenue when data acquisition or annotation is costly.

    Source: §5 · p. 8

    Even with perfectly tuned parameters, a simulation-to-real domain gap remains to be addressed.

    Source: §3 · p. 4

Abstract

from arXiv · show

Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

1 INTRODUCTION

Simulation can reduce the cost of acquiring training data, but choosing the distribution for synthetic data remains open. The paper learns simulator parameters to optimize a trained model’s validation performance, and experiments find parameters that compete with or sometimes outperform validation-set parameters.

1 INTRODUCTION

  • Simulation offers a cheaper alternative to costly annotation and can provide data for rare events, but the right synthesis distribution remains an open question.Prior approaches use human knowledge to shape simulator distributions or generate large datasets with random parameters.Source: §1 · p. 1
  • Rather than mimic real data or generate representative volume, the approach learns simulator parameters to maximize the performance of a model trained on synthesized data.The paper motivates this objective as a way to reduce reliance on human expertise and potentially use smaller datasets, while allowing the best training distribution to differ from real data.Source: §1 · pp. 1–2
  • The learned simulator parameters compete with and sometimes outperform validation-set parameters in toy and real computer-vision experiments.The reported tasks include object counting and semantic segmentation.Source: §1 · p. 2

2 METHOD

The method treats simulator adjustment as a bi-level optimization problem: choose data-generating parameters so a model trained on the resulting synthetic data performs well on validation data. Policy gradients address the non-differentiable sampling and simulator, with a policy-to-simulator interface for black-box use.

2.1 PROBLEM STATEMENT

  • The objective chooses simulator parameters to minimize validation loss for a task model trained on data sampled using those parameters.The authors use policy gradients because sampling and the simulator may be non-differentiable, and gradient-based bi-level optimization imposes additional constraints.Source: §2.1 · p. 2

2.2 LEARNING TO SIMULATE DATA WITH POLICY GRADIENTS

  • A policy samples simulator parameters, and the accuracy of the trained task model on validation data supplies its reward for learning which synthetic datasets are useful.The REINFORCE update uses an empirical estimate weighted by advantage; the baseline is an exponential moving average of previous rewards.Source: §2.2 · pp. 2–3, Figure 1 · p. 3
  • The algorithm varies task-model training epochs, generated dataset size, and whether model parameters are retained and fine-tuned or reinitialized each iteration.The interface exposes continuous simulator inputs that parameterize internal distributions; the policy is a multivariate Gaussian that optimizes its mean with variance fixed at 0.05 in the described setup.Source: §2.2 · p. 3, §2.3 · p. 4

2.3 INTERFACING A SIMULATOR WITH THE POLICY

  • The black-box simulator interface represents data generation as rendering sampled data descriptions and rendering settings, both governed by simulator parameters.Data descriptions may be modeled as a Bayesian network for ancestral sampling, while more complex models are also possible.Source: §2.3 · pp. 3–4

3 DISCUSSION AND RELATED WORK

The approach creates new simulator-generated data rather than selecting from a fixed dataset, and optimizes its distribution for task performance rather than realism. The authors note that simulation-to-real domain gap remains a separate issue even with well-tuned parameters.

3 DISCUSSION AND RELATED WORK

  • Unlike subset selection, learning to simulate creates new randomly sampled data each iteration, so it is not limited to a fixed probability distribution at acquisition.This flexibility can support generating or oversampling unusual situations that would otherwise be absent from training data.Source: §3 · p. 4
  • The method optimizes simulator parameters for a task model’s performance, unlike approaches that seek to match observed data distributions or construct image-copying programs.The learned distribution need not match the observed distribution.Source: §3 · p. 4
  • Tuning simulator parameters does not eliminate the simulation-to-real domain gap, which the authors regard as an orthogonal problem.The discussion situates domain-gap bridging as a separate focus of prior work.Source: §3 · p. 4

4 EXPERIMENTS

The experiments evaluate learning-to-simulate in a controlled toy setting, analyze its behavior on car counting, and demonstrate it on semantic segmentation with simulated and real data.

4.1 TOY EXPERIMENTS ON GAUSSIAN MIXTURES

  • In the toy experiment, policy optimization finds simulator parameters that yield good decision boundaries even though the simulator’s sampled distribution differs from the test distribution.The simulator uses fewer Gaussian-mixture components per class than the real-data distribution.Source: §4.1 · p. 5, Figure 2 · p. 5

4.2 GENERATING DATA WITH A PARAMETERIZED TRAFFIC SIMULATOR

  • The traffic experiments use a scene model for intersections, cars, roads, and buildings, paired with rendering parameters such as weather, to generate novel scenes.The authors report that substantial extensions were needed to generate different scenes rather than only different views of a static map.Source: §4.2 · pp. 5–6

4.3 LEARNING TO SIMULATE ON A HIGH-LEVEL VISION TASK

  • For car counting, learning-to-simulate reaches validation and unseen-test rewards comparable to training with ψreal; adversarial initialization converges more slowly, and results are stable across random initializations.With accumulated training, the learned policy exceeds fixed final-policy and random-parameter baselines, approaches validation parameters, and converges even when the main-task model trains for only one epoch; the epoch result may depend on task complexity.Source: §4.3 · pp. 6–7, Figure 5 · p. 7

4.4 SEMANTIC SEGMENTATION ON SIMULATED DATA

  • On simulated semantic segmentation, the learned policy outperforms the moderately crowded, unbalanced validation-scene parameters, showing those parameters are not always optimal for training.The task measures car intersection-over-union (IoU).Source: §4.4 · p. 7

4.5 SEMANTIC SEGMENTATION ON REAL DATA

  • On the unseen KITTI test set, learning-to-simulate achieves superior results to random-parameter training and outperforms random search.The table reports Car IoU for a ResNet-50 trained for 600 epochs, with the selected epoch chosen by validation reward; it also includes training on 982 annotated KITTI images as a reference.Source: Table 1 · p. 8, §4.5 · p. 8

5 CONCLUSION

Learning to simulate adjusts simulator parameters so synthetic training data helps a model achieve high validation and test accuracy. Experiments demonstrate the approach on synthetic and real data; future work includes broader segmentation labels, object detection, and dynamic memory.

5 CONCLUSION

  • The approach adjusts simulator parameters to generate synthetic data for training models toward high validation and test accuracy.The authors demonstrate it on synthetic and real data.Source: §5 · p. 8
  • Future work will expand segmentation labels, apply the algorithm to object detection, and explore dynamic memory for previously generated data.Source: §5 · p. 8

A TRAFFIC SCENE MODEL

The traffic scene model builds rendered scenes from variable roads, intersections, cars, houses, and weather. Its parameters control object presence and types, road length, and weather selection.

A TRAFFIC SCENE MODEL

  • Scenes contain variable-length roads, an L, T, or X intersection, five randomly spawned car types, houses, and four weather types.Source: A TRAFFIC SCENE MODEL · p. 11
  • Scenes are generated block by block, with car and house presence in each road block controlled by Bernoulli probabilities.Car types are selected categorically using five parameters.Source: A TRAFFIC SCENE MODEL · p. 11
  • Intersection distance is categorically sampled across lengths of 8–18 blocks, weather is categorically sampled across four types, and intersection shapes are equally likely.Source: A TRAFFIC SCENE MODEL · p. 11

B QUANTITATIVE RESULTS ON TOY EXPERIMENT

On the toy classification problem, learning to simulate yields better classification results than training on data generated with test-set distribution parameters.

B QUANTITATIVE RESULTS ON TOY EXPERIMENT

C LEARNING OF PARAMETERS IN CAR COUNTING

The section visualizes how weather-type and car-type parameters evolve during learning in the car-counting task.

C LEARNING OF PARAMETERS IN CAR COUNTING

D CAR COUNTING DATASET SIZE

The car-counting experiment examines how many images to generate per policy iteration. A dataset size of 20 suffices for convergence to good scene parameters, reducing training time.

D CAR COUNTING DATASET SIZE

E REPRODUCIBILITY

The authors test whether learning to simulate converges across random seeds in car counting. Both reward measures converge across the three seeds.

E REPRODUCIBILITY

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