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Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior.
Htn planning: complexity and expressivity
Erol, K., Hendler, J., and Nau, D. S · 1994
Earlier work this paper cites.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Kulkarni, T. D., Narasimhan, K., Saeedi, A., and Tenenbaum, J · 2016
Earlier work this paper cites.
The option-critic architecture
Bacon, P.-L., Harb, J., and Precup, D · 2017
Earlier work this paper cites.
Alfworld: Aligning text and embodied environments for interactive learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M · 2020
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al · 2022
Earlier work this paper cites.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2022
Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
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A survey of embodied ai: From simulators to research tasks
Duan, J., Yu, S., Tan, H. L., Zhu, H., and Tan, C · 2022
Earlier work this paper cites.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D.-A., Zhu, Y., and Anandkumar, A · 2022
Earlier work this paper cites.
Don’t generate, discriminate: A proposal for grounding language models to real-world environments
Gu, Y., Deng, X., and Su, Y · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Le, H., Wang, Y., Gotmare, A. D., Savarese, S., and Hoi, S. C. H · 2022
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Skill induction and planning with latent language
Sharma, P., Torralba, A., and Andreas, J · 2022
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Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., Ibarz, J., Irpan, A., Jang, E., Julian, R., et al · 2023
Cited alongside, same era.
Large language models as tool makers
Cai, T., Wang, X., Ma, T., Chen, X., and Zhou, D · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D · 2023
Cited alongside, same era.
Large language models cannot self-correct reasoning yet
Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D · 2023
Cited alongside, same era.
Ds-1000: A natural and reliable benchmark for data science code generation
Lai, Y., Li, C., Wang, Y., Zhang, T., Zhong, R., Zettlemoyer, L., Yih, W.-t., Fried, D., Wang, S., and Yu, T · 2023
Toolformer: Language models can teach themselves to use tools, 2023
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. R., and Yao, S · 2023
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Progprompt: Generating situated robot task plans using large language models
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., and Garg, A · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Song, C. H., Wu, J., Washington, C., Sadler, B. M., Chao, W.-L., and Su, Y · 2023
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Adaplanner: Adaptive planning from feedback with language models, 2023
Sun, H., Zhuang, Y., Kong, L., Dai, B., and Zhang, C · 2023
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Cited alongside, same era.
Code as policies: Language model programs for embodied control
Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., and Zeng, A · 2023
Cited alongside, same era.
Llm+ p: Empowering large language models with optimal planning proficiency
Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., and Stone, P · 2023
Cited alongside, same era.
Emergent agentic transformer from chain of hindsight experience
Liu, H. and Abbeel, P · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S · 2023
Cited alongside, same era.
Creator: Disentangling abstract and concrete reasonings of large language models through tool creation, 2023
Qian, C., Han, C., Fung, Y. R., Qin, Y., Liu, Z., and Ji, H · 2023
Cited alongside, same era.
Taskweaver: A code-first agent framework
Qiao, B., Li, L., Zhang, X., He, S., Kang, Y., Zhang, C., Yang, F., Dong, H., Zhang, J., Wang, L., et al · 2023
Cited alongside, same era.
Wong, L., Mao, J., Sharma, P., Siegel, Z. S., Feng, J., Korneev, N., Tenenbaum, J. B., and Andreas, J · 2023
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Spring: Gpt-4 out-performs rl algorithms by studying papers and reasoning
Wu, Y., Min, S. Y., Prabhumoye, S., Bisk, Y., Salakhutdinov, R., Azaria, A., Mitchell, T., and Li, Y · 2023
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Openagents: An open platform for language agents in the wild
Xie, T., Zhou, F., Cheng, Z., Shi, P., Weng, L., Liu, Y., Hua, T. J., Zhao, J., Liu, Q., Liu, C., et al · 2023
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Lemur: Harmonizing natural language and code for language agents
Xu, Y., Su, H., Xing, C., Mi, B., Liu, Q., Shi, W., Hui, B., Zhou, F., Liu, Y., Xie, T., et al · 2023
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Craft: Customizing llms by creating and retrieving from specialized toolsets
Yuan, L., Chen, Y., Wang, X., Fung, Y. R., Peng, H., and Ji, H · 2023
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Expel: Llm agents are experiential learners
Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., and Huang, G · 2023
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Agentboard: An analytical evaluation board of multi-turn llm agents, 2024
Ma, C., Zhang, J., Zhu, Z., Yang, C., Yang, Y., Jin, Y., Lan, Z., Kong, L., and He, J · 2024
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Regal: Refactoring programs to discover generalizable abstractions
Stengel-Eskin, E., Prasad, A., and Bansal, M · 2024
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