Self-Supervised Policy Adaptation during Deployment

In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse environments from DeepMind Control suite and ViZDoom. Our method improves generalization in 25 out of 30 environments across various tasks, and outperforms domain randomization on a majority of environments.

[1]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[2]  Benjamin Recht,et al.  Do Image Classifiers Generalize Across Time , 2019 .

[3]  Paolo Favaro,et al.  Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.

[4]  Nikos Komodakis,et al.  Unsupervised Representation Learning by Predicting Image Rotations , 2018, ICLR.

[5]  Marcin Andrychowicz,et al.  Asymmetric Actor Critic for Image-Based Robot Learning , 2017, Robotics: Science and Systems.

[6]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[7]  Dawn Xiaodong Song,et al.  Assessing Generalization in Deep Reinforcement Learning , 2018, ArXiv.

[8]  Geoffrey E. Hinton,et al.  A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.

[9]  Michal Irani,et al.  "Zero-Shot" Super-Resolution Using Deep Internal Learning , 2017, CVPR.

[10]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Balaraman Ravindran,et al.  EPOpt: Learning Robust Neural Network Policies Using Model Ensembles , 2016, ICLR.

[12]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[13]  Matthias Bethge,et al.  Generalisation in humans and deep neural networks , 2018, NeurIPS.

[14]  Deva Ramanan,et al.  Online Model Distillation for Efficient Video Inference , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[15]  Alexei A. Efros,et al.  Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[17]  Abhinav Gupta,et al.  Unsupervised Learning of Visual Representations Using Videos , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Kibok Lee,et al.  A Simple Randomization Technique for Generalization in Deep Reinforcement Learning , 2019, ICLR 2020.

[19]  Bolei Zhou,et al.  Semantic photo manipulation with a generative image prior , 2019, ACM Trans. Graph..

[20]  Ali Razavi,et al.  Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.

[21]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[22]  Marek Wydmuch,et al.  ViZDoom Competitions: Playing Doom From Pixels , 2018, IEEE Transactions on Games.

[23]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Benjamin Recht,et al.  Do ImageNet Classifiers Generalize to ImageNet? , 2019, ICML.

[25]  Dawn Song,et al.  Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty , 2019, NeurIPS.

[26]  Stella X. Yu,et al.  Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Tom Schaul,et al.  Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.

[28]  Kaiming He,et al.  Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Marcin Andrychowicz,et al.  Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Gaurav S. Sukhatme,et al.  Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning , 2020 .

[31]  Jakub W. Pachocki,et al.  Learning dexterous in-hand manipulation , 2018, Int. J. Robotics Res..

[32]  Wojciech Zaremba,et al.  Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[33]  Abhinav Gupta,et al.  Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[34]  Yuval Tassa,et al.  DeepMind Control Suite , 2018, ArXiv.

[35]  Alexei A. Efros,et al.  Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Alexei A. Efros,et al.  Colorful Image Colorization , 2016, ECCV.

[37]  Shai Bagon,et al.  InGAN: Capturing and Remapping the "DNA" of a Natural Image , 2018 .

[38]  Jürgen Schmidhuber,et al.  Recurrent World Models Facilitate Policy Evolution , 2018, NeurIPS.

[39]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[40]  Mengjie Zhang,et al.  Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[41]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Martin A. Riedmiller,et al.  Deep auto-encoder neural networks in reinforcement learning , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[43]  Pieter Abbeel,et al.  Learning Predictive Representations for Deformable Objects Using Contrastive Estimation , 2020, CoRL.

[44]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[45]  Benjamin Recht,et al.  A systematic framework for natural perturbations from videos , 2019, ArXiv.

[46]  Nitish Srivastava Unsupervised Learning of Visual Representations using Videos , 2015 .

[47]  Abhinav Gupta,et al.  Robust Adversarial Reinforcement Learning , 2017, ICML.

[48]  Andrew Zisserman,et al.  Multi-task Self-Supervised Visual Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[49]  Thomas G. Dietterich,et al.  Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations , 2018, 1807.01697.

[50]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[51]  Alexei A. Efros,et al.  Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[52]  Hongyuan Zha,et al.  Single Episode Policy Transfer in Reinforcement Learning , 2019, ICLR.

[53]  Sergey Levine,et al.  (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image , 2016, Robotics: Science and Systems.

[54]  Alexei A. Efros,et al.  Test-Time Training with Self-Supervision for Generalization under Distribution Shifts , 2020, ICML.

[55]  Pieter Abbeel,et al.  CURL: Contrastive Unsupervised Representations for Reinforcement Learning , 2020, ICML.

[56]  Dacheng Tao,et al.  Domain Generalization via Conditional Invariant Representations , 2018, AAAI.

[57]  Michael I. Jordan,et al.  Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.

[58]  Benjamin Recht,et al.  Do CIFAR-10 Classifiers Generalize to CIFAR-10? , 2018, ArXiv.

[59]  Henry Zhu,et al.  Soft Actor-Critic Algorithms and Applications , 2018, ArXiv.

[60]  Razvan Pascanu,et al.  Progressive Neural Networks , 2016, ArXiv.

[61]  Alexei A. Efros,et al.  Unsupervised Domain Adaptation through Self-Supervision , 2019, ArXiv.

[62]  Pieter Abbeel,et al.  Reinforcement Learning with Augmented Data , 2020, NeurIPS.

[63]  Allan Jabri,et al.  Learning Correspondence From the Cycle-Consistency of Time , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[64]  Michal Irani,et al.  InGAN: Capturing and Retargeting the “DNA” of a Natural Image , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[65]  Sergey Levine,et al.  Visual Reinforcement Learning with Imagined Goals , 2018, NeurIPS.

[66]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[67]  Sergey Levine,et al.  Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[68]  Sergey Levine,et al.  Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[69]  Sergey Levine,et al.  QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.

[70]  Dacheng Tao,et al.  Domain Generalization via Conditional Invariant Representation , 2018, ArXiv.

[71]  Pieter Abbeel,et al.  Planning to Explore via Self-Supervised World Models , 2020, ICML.

[72]  Dieter Fox,et al.  BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators , 2019, Robotics: Science and Systems.

[73]  Ilya Kostrikov,et al.  Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels , 2020, ArXiv.

[74]  Sergey Levine,et al.  Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control , 2018, ArXiv.

[75]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[76]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[77]  Eugenio Culurciello,et al.  Continual Reinforcement Learning in 3D Non-stationary Environments , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[78]  Ali Farhadi,et al.  Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[79]  Joelle Pineau,et al.  Improving Sample Efficiency in Model-Free Reinforcement Learning from Images , 2019, AAAI.

[80]  Tatsuya Harada,et al.  Domain Generalization Using a Mixture of Multiple Latent Domains , 2019, AAAI.