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Simple Noisy Environment Augmentation for Reinforcement Learning

arXiv 2305.02882 ↗First posted 4 May 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Data augmentation is a widely used technique for improving model performance in machine learning, particularly in computer vision and natural language processing. Recently, there has been increasing interest in applying augmentation techniques to reinforcement learning (RL) problems, with a focus on image-based augmentation. In this paper, we explore a set of generic wrappers designed to augment RL environments with noise and encourage agent exploration and improve training data diversity which are applicable to a broad spectrum of RL algorithms and environments. Specifically, we concentrate on augmentations concerning states, rewards, and transition dynamics and introduce two novel augmentation techniques. In addition, we introduce a noise rate hyperparameter for control over the frequency of noise injection. We present experimental results on the impact of these wrappers on return using three popular RL algorithms, Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), and Proximal Policy Optimization (PPO), across five MuJoCo environments. To support the choice of augmentation technique in practice, we also present analysis that explores the performance these techniques across environments. Lastly, we publish the wrappers in our noisyenv repository for use with gym environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 49 references that are in the map. First posted on arXiv 4 May 2023.

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36 of 49 references.

References · 36

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24 more
  1. 2020Reinforcement Learning with Augmented Datacited by 452
  2. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
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  17. 2021Memory-Augmented Reinforcement Learning for Image-Goal Navigationcited by 64
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  22. 2019Domain Randomization for Active Pose Estimationcited by 11
  23. 2021Adversarial Imitation Learning with Trajectorial Augmentation and Correctioncited by 11
  24. 2019Reinforcement Learning in Non-Stationary Environmentscited by 10

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