Journal Home
Login
Register
Open Access Journal
Submit a Paper
Propose a Special lssue
Open Access
ARTICLE
BRIDGING THE GENERALIZATION GAP IN VISUAL REINFORCEMENT LEARNING: A THEORETICAL AND EMPIRICAL STUDY
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
pdf
Abstract
Visual Reinforcement Learning (VRL) agents frequently suffer from a significant "generalization gap," exhibiting degraded performance when deployed in environments that subtly differ from their training conditions. This paper provides a comprehensive analysis of the factors contributing to this discrepancy, integrating theoretical insights with empirical evidence. We categorize and discuss various strategies employed to bridge this gap, including the pivotal roles of data augmentation, advanced representation learning techniques (such as self-supervised and invariant learning), regularization methods, domain randomization for sim-to-real transfer, and the integration of auxiliary tasks and structured policy approaches. Our findings underscore the importance of learning robust, invariant visual representations and the efficacy of exposing agents to diverse, augmented experiences. We highlight the ongoing challenges, particularly in quantifying and optimizing for true environmental invariance, and propose future research directions aimed at developing more adaptable and generalizable VRL systems capable of thriving in varied real-world scenarios.
Keywords
References
1. Agarwal, A., Hsu, D. J., Kale, S., Langford, J., Li, L., & Schapire, R. E. (2014). Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. In International Conference on Machine Learning.
2. Agarwal, R., Machado, M. C., Castro, P. S., & Bellemare, M. G. (2021). Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. In International Conference on Learning Representations.
3. Agrawal, S., & Goyal, N. (2012). Thompson Sampling for Contextual Bandits with Linear Payoffs. In International Conference on Machine Learning.
4. Bahl, S., Mukadam, M., Gupta, A. K., & Pathak, D. (2020). Neural Dynamic Policies for End-to-End Sensorimotor Learning. In Neural Information Processing Systems.
5. Bauer, M., & Mnih, A. (2021). Generalized Doubly Reparameterized Gradient Estimators. In International Conference on Machine Learning.
6. Bertoin, D., Zouitine, A., Zouitine, M., & Rachelson, E. (2022). Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning. In Neural Information Processing Systems.
7. Bertrán, M., Martínez, N., Phielipp, M., & Sapiro, G. (2020). Instance based Generalization in Reinforcement Learning. In Neural Information Processing Systems.
8. Bradtke, S. J. (1992). Reinforcement Learning Applied to Linear Quadratic Regulation. In Neural Information Processing Systems.
9. Cao, Y., & Ren, W. (2010). Optimal Linear-Consensus Algorithms: An LQR Perspective. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40, 819–830.
10. Cao, Y., Li, Z., Yang, T., Zhang, H., Zheng, Y., Li, Y., Hao, J., & Liu, Y. (2022). Galois: Boosting deep reinforcement learning via generalizable logic synthesis. ArXiv, abs/2205.13728.
11. Chebotar, Y., Handa, A., Makoviychuk, V., Macklin, M., Issac, J., Ratliff, N. D., & Fox, D. (2018). Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience. In 2019 International Conference on Robotics and Automation (ICRA).
12. Ciosek, K., & Whiteson, S. (2020). Expected Policy Gradients for Reinforcement Learning. Journal of Machine Learning Research, 21(52), 1–51.
13. Cobbe, K., Klimov, O., Hesse, C., Kim, T., & Schulman, J. (2018). Quantifying Generalization in Reinforcement Learning. In International Conference on Machine Learning.
14. Cole, E., Yang, X. S., Wilber, K., Aodha, O. M., & Belongie, S. J. (2021). When Does Contrastive Visual Representation Learning Work?. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
15. Dabney, W., Ostrovski, G., Silver, D., & Munos, R. (2018). Implicit Quantile Networks for Distributional Reinforcement Learning. In International Conference on Machine Learning.
16. Dai, T., Arulkumaran, K., Tukra S., Behbahani, F. M. P., & Bharath, A. A. (2019). Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation. Neurocomputing, 493, 143–165.
17. Delfosse, Q., Shindo, H., Dhami, D. S., & Kersting, K. (2023). Interpretable and explain-able logical policies via neurally guided symbolic abstraction. In Neural Information Processing Systems.
18. Delfosse, Q., Sztwiertnia, S., Stammer, W., Rothermel, M., & Kersting, K. (2024). Interpretable concept bottlenecks to align reinforcement learning agents. ArXiv, abs/2401.05821.
19. Derman, E., Mankowitz, D., Mann, T., & Mannor, S. (2020). A bayesian approach to robust reinforcement learning. In Uncertainty in Artificial Intelligence.
20. Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., Locatello, F., Bachem, O., Schölkopf, B., & Bauer, S. (2021). The Role of Pretrained Rep-resentations for the OOD Generalization of Reinforcement Learning Agents. arXiv, arXiv/2107.05686.
21. Doersch, C., Gupta, A. K., & Efros, A. A. (2015). Unsupervised Visual Representation Learning by Context Prediction. In 2015 IEEE International Conference on Computer Vision (ICCV).
22. D’Oro, P., & Ja´skowski, W. (2020). How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. In Neural Information Processing Systems.
23. Fan, L. J., Wang, G., Huang, D.-A., Yu, Z., Fei-Fei, L., Zhu, Y., & Anandkumar, A. (2021). SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies. In International Conference on Machine Learning.
24. Fazlyab, M., Robey, A., Hassani, H., Morari, M., & Pappas, G. J. (2019). Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks. In Neural Information Processing Systems.
25. Figurnov, M., Mohamed, S., & Mnih, A. (2018). Implicit Reparameterization Gradients. In Neural Information Processing Systems.
26. Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., & Abbeel, P. (2015). Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders. ArXiv, abs/1509.06113.
27. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M., & Mocanu, D. C. (2023). Automatic noise filtering with dynamic sparse training in deep reinforcement learning. In Adaptive Agents and Multi-Agent Systems.
28. Gumbel, E. J. (1954). Statistical theory of extreme values and some practical applications : A series of lectures. Technical report.
29. Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018a). Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. In Inter-national Conference on Machine Learning.
30. Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., & Levine, S. (2018b). Soft Actor-Critic Algorithms and Applications. ArXiv, abs/1812.05905.
31. Hafner, D., Lillicrap, T. P., Fischer, I. S., Villegas, R., Ha, D. R., Lee, H., & Davidson, J. (2018). Learning Latent Dynamics for Planning from Pixels. In International Conference on Machine Learning.
32. Hansen, N., Jangir, R., Sun, Y., Aleny`a, G., Abbeel, P., Efros, A. A., Pinto, L., & Wang, X. (2021a). Self-Supervised Policy Adaptation during Deployment. In International Conference on Learning Representations.
33. Hansen, N., Su, H., & Wang, X. (2021b). Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation. In Neural Information Processing Systems.
34. Hansen, N., & Wang, X. (2020). Generalization in Reinforcement Learning by Soft Data Augmentation. In 2021 IEEE International Conference on Robotics and Automation (ICRA).
35. Hansen, N., Wang, X., & Su, H. (2022). Temporal Difference Learning for Model Predictive Control. In International Conference on Machine Learning.
36. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
37. Heess, N. M. O., Wayne, G., Silver, D., Lillicrap, T. P., Erez, T., & Tassa, Y. (2015). Learning Continuous Control Policies by Stochastic Value Gradients. In Neural Information Processing Systems.
38. Huang, Y., Peng, P., Zhao, Y., Chen, G., & Tian, Y. (2022). Spectrum Random Masking for Generalization in Image-based Reinforcement Learning. In Neural Information Processing Systems.
39. Huijben, I. A. M., Kool, W., Paulus, M. B., & van Sloun, R. J. G. (2021). A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 1353–1371.
40. Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., & Kavukcuoglu, K. (2017). Reinforcement Learning with Unsupervised Auxiliary Tasks. In International Conference on Learning Representations.
41. Jaksch, T., Ortner, R., & Auer, P. (2008). Near-optimal Regret Bounds for Reinforcement Learning. Journal of Machine Learning Research, 11, 1563–1600.
42. Jang, E., Gu, S., & Poole, B. (2017). Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations.
43. Jiang, Z., & Luo, S. (2019). Neural logic reinforcement learning. In International Conference on Machine Learning.
44. Joo, W., Kim, D., Shin, S.-J., & Moon, I.-C. (2020). Generalized Gumbel-Softmax Gradient Estimator for Various Discrete Random Variables. ArXiv, abs/2003.01847.
45. Jost, M., Pannocchia, G., & M ̈onnigmann, M. (2017). Accelerating Linear Model Predictive Control by Constraint Removal. European Journal of Control, 35, 42–49.
46. Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. In Neural Information Processing Systems.
47. Kingma, D. P., & Welling, M. (2013). Auto-encoding Variational Bayes. arXiv, arXiv/1312.6114.
48. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Doll´ar, P., & Girshick, R. B. (2023). Segment Anything. ArXiv, abs/2304.02643.
49. Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2019a). Big Transfer (BiT): General Visual Representation Learning. In European Conference on Computer Vision.
50. Kolesnikov, A., Zhai, X., & Beyer, L. (2019b). Revisiting Self-Supervised Visual Represen-tation Learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
51. Kulh ́anek, J., Derner, E., de Bruin, T., & Babuka, R. (2019). Vision-based Navigation Using Deep Reinforcement Learning. In 2019 European Conference on Mobile Robots (ECMR).
52. Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2020). Reinforcement Learning with Augmented Data. In Neural Information Processing Systems.
53. Le Lan, C., Tu, S., Oberman, A., Agarwal, R., & Bellemare, M. G. (2022). On the Gener-alization of Representations in Reinforcement Learning. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.
54. Lecarpentier, E., Abel, D., Asadi, K., Jinnai, Y., Rachelson, E., & Littman, M. L. (2020). Lipschitz Lifelong Reinforcement Learning. In AAAI Conference on Artificial Intelli-gence.
55. Lee, A. X., Nagabandi, A., Abbeel, P., & Levine, S. (2019a). Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. In Neural Information Processing Systems.
56. Lee, K., Lee, K., Shin, J., & Lee, H. (2019b). Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning. In International Conference on Learning Representations.
57. Li, H., Pan, S. J., Wang, S., & Kot, A. C. (2018). Domain Generalization with Adversar-ial Feature Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5400–5409.
58. Li, L., Lyu, J., Ma, G., Wang, Z., Yang, Z., Li, X., & Li, Z. (2023). Normalization Enhances Generalization in Visual Reinforcement Learning. ArXiv, abs/2306.00656.
59. Li, Z., Ratliff, L. J., Nassif, H., Jamieson, K. G., & Jain, L. P. (2022). Instance-optimal PAC Algorithms for Contextual Bandits. In Neural Information Processing Systems.
60. Lin, X., Baweja, H. S., Kantor, G. A., & Held, D. (2019). Adaptive Auxiliary Task Weighting for Reinforcement Learning. In Neural Information Processing Systems.
61. Liu, H.-T. D., Williams, F., Jacobson, A., Fidler, S., & Litany, O. (2022). Learning Smooth Neural Functions via Lipschitz Regularization. In ACM SIGGRAPH 2022 Conference Proceedings.
62. Lorberbom, G., Johnson, D. D., Maddison, C. J., Tarlow, D., & Hazan, T. (2021). Learning Generalized Gumbel-max Causal Mechanisms. In Neural Information Processing Systems.
63. Luo, L., Zhang, G., Xu, H., Yang, Y., Fang, C., & Li, Q. (2024). Insight: End-to-end neuro-symbolic visual reinforcement learning with language explanations. ArXiv, abs/2403.12451.
64. Lyle, C., Rowland, M., Ostrovski, G., & Dabney, W. (2021). On The Effect of Auxiliary Tasks on Representation Dynamics. In International Conference on Artificial Intelli-gence and Statistics.
65. Lyu, J., Wan, L., Li, X., & Lu, Z. (2024). Off-policy rl algorithms can be sample-efficient for continuous control via sample multiple reuse. Information Sciences, 666, 120371.
