Abstract
Vision-and-Language Navigation (VLN) requires an agent to find a specified spot in an unseen environment by following natural language instructions. Dominant methods based on supervised learning clone expert’s behaviours and thus perform better on seen environments, while showing restricted performance on unseen ones. Reinforcement Learning (RL) based models show better generalisation ability but have issues as well, requiring large amount of manual reward engineering is one of which. In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task. Our proposed method consists of two complementary components: Soft Expert Distillation (SED) module encourages agents to behave like an expert as much as possible, but in a soft fashion; Self Perceiving (SP) module targets at pushing the agent towards the final destination as fast as possible. Empirically, we evaluate our model on the VLN seen, unseen and test splits and the model outperforms the state-of-the-art methods on most of the evaluation metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The VLN leaderboard address is https://evalai.cloudcv.org/web/challenges/challenge-page/97/leaderboard/270.
References
Anderson, P., et al.: On evaluation of embodied navigation agents. arXiv preprint arXiv:1807.06757 (2018)
Anderson, P., et al.: Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3674–3683 (2018)
Brockman, G., et al.: Openai gym. arXiv preprint arXiv:1606.01540 (2016)
Fried, D., et al.: Speaker-follower models for vision-and-language navigation. In: Advances in Neural Information Processing Systems, pp. 3314–3325 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ho, J., Ermon, S.: Generative adversarial imitation learning. In: Advances in Neural Information Processing Systems, pp. 4565–4573 (2016)
Ke, L., et al.: Tactical rewind: self-correction via backtracking in vision-and-language navigation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6741–6749 (2019)
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)
Ma, C.Y., et al.: Self-monitoring navigation agent via auxiliary progress estimation. arXiv preprint arXiv:1901.03035 (2019)
Ma, C.Y., Wu, Z., AlRegib, G., Xiong, C., Kira, Z.: The regretful agent: heuristic-aided navigation through progress estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6732–6740 (2019)
Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)
Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Ng, A.Y., Russell, S.J., et al.: Algorithms for inverse reinforcement learning. In: ICML, vol. 1, pp. 663–670 (2000)
Reddy, S., Dragan, A.D., Levine, S.: SQIL: imitation learning via regularized behavioral cloning. arXiv preprint arXiv:1905.11108 (2019)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Tan, H., Yu, L., Bansal, M.: Learning to navigate unseen environments: back translation with environmental dropout. arXiv preprint arXiv:1904.04195 (2019)
Todorov, E., Erez, T., Tassa, Y.: Mujoco: a physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026–5033. IEEE (2012)
Wang, R., Ciliberto, C., Amadori, P., Demiris, Y.: Random expert distillation: imitation learning via expert policy support estimation. arXiv preprint arXiv:1905.06750 (2019)
Wang, X., et al.: Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6629–6638 (2019)
Yadav, D., et al.: EvalAI: towards better evaluation systems for AI agents (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Wu, Q., Shen, C. (2020). Soft Expert Reward Learning for Vision-and-Language Navigation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-58545-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58544-0
Online ISBN: 978-3-030-58545-7
eBook Packages: Computer ScienceComputer Science (R0)