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In recent years, reinforcement learning (RL)-based methods for learning driving policies have gained increasing attention in the autonomous driving community and have achieved remarkable progress in various driving scenarios.
The tacit dimension, in: Knowledge in organisations. Routledge, pp. 135–146
Polanyi, M., 2009 · 2009
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A finite time analysis of temporal difference learning with linear function approximation, in: Conference on learning theory, PMLR. pp. 1691–1692
Bhandari, J., Russo, D., Singal, R., 2018 · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, in: International conference on machine learning, PMLR. pp. 1861–1870
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S., 2018 · 2018
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An environment for autonomous driving decision-making
Leurent, E., 2018 · 2018
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A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to ai-guided driving policy learning
Di, X., Shi, R., 2021 · 2021
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Learning transferable visual models from natural language supervision, in: International conference on machine learning, PMLR. pp. 8748–8763
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., 2021 · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N., 2021 · 2021
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Trustworthy safety improvement for autonomous driving using reinforcement learning
Cao, Z., Xu, S., Jiao, X., Peng, H., Yang, D., 2022 · 2022
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Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Chen, J., Li, S.E., Tomizuka, M., 2022 · 2022
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Zero-shot reward specification via grounded natural language, in: International Conference on Machine Learning, PMLR. pp. 14743–14752
Mahmoudieh, P., Pathak, D., Darrell, T., 2022 · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al., 2022 · 2022
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Language reward modulation for pretraining reinforcement learning
Adeniji, A., Xie, A., Sferrazza, C., Seo, Y., James, S., Abbeel, P., 2023 · 2023
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Vision-language models as a source of rewards, in: Second Agent Learning in Open-Endedness Workshop at NeurIPS 2023
Baumli, K., Singh, S., Behbahani, F., Chan, H., Comanici, G., Flennerhag, S., Gazeau, M., Holsheimer, K., Horgan, D., Laskin, M., et al., 2023 · 2023
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Dense reinforcement learning for safety validation of autonomous vehicles
Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., Liu, H.X., 2023 · 2023
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Reward (mis) design for autonomous driving
Knox, W.B., Allievi, A., Banzhaf, H., Schmitt, F., Stone, P., 2023 · 2023
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Reward design with language models
Kwon, M., Xie, S.M., Bullard, K., Sadigh, D., 2023 · 2023
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Eureka: Human-level reward design via coding large language models
Ma, Y.J., Liang, W., Wang, G., Huang, D.A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., Anandkumar, A., 2023 · 2023
Cited alongside, same era.
Efficient reinforcement learning for autonomous driving with parameterized skills and priors, in: Robotics: Science and Systems (RSS 2023)
Wang, L., Liu, J., Shao, H., Wang, W., Chen, R., Liu, Y., Waslander, S.L., 2023a · 2023
Cited alongside, same era.
Dilu: A knowledge-driven approach to autonomous driving with large language models
Wen, L., Fu, D., Li, X., Cai, X., Ma, T., Cai, P., Dou, M., Shi, B., He, L., Qiao, Y., 2023 · 2023
Cited alongside, same era.
Goal-lbp: Goal-based local behavior guided trajectory prediction for autonomous driving
Yao, Z., Li, X., Lang, B., Chuah, M.C., 2023 · 2023
Cited alongside, same era.
Vision-language models are zero-shot reward models for reinforcement learning
Rocamonde, J., Montesinos, V., Nava, E., Perez, E., Lindner, D., 2024 · 2024
Closest in time.
Lmdrive: Closed-loop end-to-end driving with large language models, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15120–15130
Shao, H., Hu, Y., Wang, L., Song, G., Waslander, S.L., Liu, Y., Li, H., 2024 · 2024
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Roboclip: One demonstration is enough to learn robot policies
Sontakke, S., Zhang, J., Arnold, S., Pertsch, K., B i · 2024
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Drivevlm: The convergence of autonomous driving and large vision-language models
Tian, X., Gu, J., Li, B., Liu, Y., Wang, Y., Zhao, Z., Zhan, K., Jia, P., Lang, X., Zhao, H., 2024 · 2024
Closest in time.
Code as reward: Empowering reinforcement learning with vlms
Venuto, D., Islam, S.N., Klissarov, M., Precup, D., Yang, S., Anand, A., 2024 · 2024
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Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.H., Arenas, M.G., Chiang, H.T.L., Erez, T., Hasenclever, L., Humplik, J., et al., 2023 · 2023
Cited alongside, same era.
A review of reward functions for reinforcement learning in the context of autonomous driving
Abouelazm, A., Michel, J., Zoellner, J.M., 2024 · 2024
Cited alongside, same era.
Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 902–909
Cui, C., Ma, Y., Cao, X., Ye, W., Wang, Z., 2024 · 2024
Cited alongside, same era.
Furl: Visual-language models as fuzzy rewards for reinforcement learning
Fu, Y., Zhang, H., Wu, D., Xu, W., Boulet, B., 2024 · 2024
Cited alongside, same era.
Modeling coupled driving behavior during lane change: A multi-agent transformer reinforcement learning approach
Guo, H., Keyvan-Ekbatani, M., Xie, K., 2024 · 2024
Cited alongside, same era.
Autoreward: Closed-loop reward design with large language models for autonomous driving
Han, X., Yang, Q., Chen, X., Cai, Z., Chu, X., Zhu, M., 2024 · 2024
Cited alongside, same era.
Towards robust car-following based on deep reinforcement learning
Hart, F., Okhrin, O., Treiber, M., 2024 · 2024
Cited alongside, same era.
Revolve: Reward evolution with large language models for autonomous driving
Hazra, R., Sygkounas, A., Persson, A., Loutfi, A., Martires, P.Z.D., 2024 · 2024
Cited alongside, same era.
Closest in time.
Rl-vlm-f: Reinforcement learning from vision language foundation model feedback
Wang, Y., Sun, Z., Zhang, J., Xian, Z., Biyik, E., Held, D., Erickson, Z., 2024 · 2024
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Editable scene simulation for autonomous driving via collaborative llm-agents, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15077–15087
Wei, Y., Wang, Z., Lu, Y., Xu, C., Liu, C., Zhao, H., Chen, S., Wang, Y., 2024 · 2024
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Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Wu, J., Huang, C., Huang, H., Lv, C., Wang, Y., Wang, F.Y., 2024 · 2024
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Text2reward: Reward shaping with language models for reinforcement learning, in: The Twelfth International Conference on Learning Representations
Xie, T., Zhao, S., Wu, C.H., Liu, Y., Luo, Q., Zhong, V., Yang, Y., Yu, T., 2024 · 2024
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
Xu, Z., Zhang, Y., Xie, E., Zhao, Z., Guo, Y., Wong, K.Y.K., Li, Z., Zhao, H., 2024 · 2024
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Lord: Large models based opposite reward design for autonomous driving
Ye, X., Tao, F., Mallik, A., Yaman, B., Ren, L., 2024 · 2024
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Highwayllm: Decision-making and navigation in highway driving with rl-informed language model
Yildirim, M., Dagda, B., Fallah, S., 2024 · 2024
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V2x-vlm: End-to-end v2x cooperative autonomous driving through large vision-language models
You, J., Shi, H., Jiang, Z., Huang, Z., Gan, R., Wu, K., Cheng, X., Li, X., Ran, B., 2024 · 2024
Closest in time.
Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15459–15469
Zhang, J., Xu, C., Li, B., 2024 · 2024
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In-context learning for automated driving scenarios
Zhou, Z., Zhang, J., Zhang, J., Wang, B., Shi, T., Khamis, A., 2024 · 2024
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