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One of the fundamental quests of AI is to produce agents that coordinate well with humans.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P. F., and Irving, G · 1909
Earlier work this paper cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2005
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A., Bagnell, J. A., and Dey, A. K · 2008
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017
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Learning social conventions in markov games
Lerer, A. and Peysakhovich, A · 2018
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On the utility of learning about humans for human-ai coordination
Carroll, M., Shah, R., Ho, M. K., Griffiths, T., Seshia, S. A., Abbeel, P., and Dragan, A. D · 2019
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Alphastar: Mastering the real-time strategy game starcraft ii
Vinyals, O., Babuschkin, I., Chung, J., Mathieu, M., Jaderberg, M., Czarnecki, W. M., Dudzik, A., Huang, A., Georgiev, P., Powell, R., et al · 2019
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The hanabi challenge: A new frontier for ai research
Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., et al · 2020
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“other-play”for zero-shot coordination
Hu, H., Peysakhovich, A., Lerer, A., and Foerster, J · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N. S., Chen, A. S., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N. D., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M. S., Krishna, R., Kuditipudi, R., and et al · 2021
Earlier work this paper cites.
K-level reasoning for zero-shot coordination in hanabi
Cui, B., Hu, H., Pineda, L., and Foerster, J. N · 2021
Cited alongside, same era.
Off-belief learning
Hu, H., Lerer, A., Cui, B., Pineda, L., Wu, D., Brown, N., and Foerster, J · 2021
Cited alongside, same era.
Trajectory diversity for zero-shot coordination
Lupu, A., Cui, B., Hu, H., and Foerster, J · 2021
Cited alongside, same era.
ELLA: exploration through learned language abstraction
Mirchandani, S., Karamcheti, S., and Sadigh, D · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
Cited alongside, same era.
Minedojo: Building open-ended embodied agents with internet-scale knowledge, 2022
Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D.-A., Zhu, Y., and Anandkumar, A · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
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Large language models are zero-shot reasoners, 2022
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Using natural language and program abstractions to instill human inductive biases in machines
Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M., Hawkins, R. D., Cohen, J., Daw, N., Narasimhan, K. R., and Griffiths, T. L · 2022
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On the critical role of conventions in adaptive human-ai collaboration
Shih, A., Sawhney, A., Kondic, J., Ermon, S., and Sadigh, D · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
Cited alongside, same era.
Do as i can and 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., Herzog, A., Ho, D., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jang, E., Ruano, R. J., Jeffrey, K., Jesmonth, S., Joshi, N., Julian, R., Kalashnikov, D., Kuang, Y., Lee, K.-H., Levine, S., Lu, Y., Luu, L., Parada, C., Pastor, P., Quiambao, J., Rao, K., Rettinghouse, J., Reyes, D., Sermanet, P., Sievers, N., Tan, C., Toshev, A., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Xu, S., Yan, M., and Zeng, A · 2022
Cited alongside, same era.
Mastering the game of no-press diplomacy via human-regularized reinforcement learning and planning
Bakhtin, A., Wu, D. J., Lerer, A., Gray, J., Jacob, A. P., Farina, G., Miller, A. H., and Brown, N · 2022
Cited alongside, same era.
Generating diverse cooperative agents by learning incompatible policies
Charakorn, R., Manoonpong, P., and Dilokthanakul, N · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models, 2022
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Castro-Ros, A., Pellat, M., Robinson, K., Valter, D., Narang, S., Mishra, G., Yu, A., Zhao, V., Huang, Y., Dai, A., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
Cited alongside, same era.
Human-level play in the game of “diplomacy” by combining language models with strategic reasoning
FAIR, Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., Hu, H., Jacob, A. P., Komeili, M., Konath, K., Kwon, M., Lerer, A., Lewis, M., Miller, A. H., Mitts, S., Renduchintala, A., Roller, S., Rowe, D., Shi, W., Spisak, J., Wei, A., Wu, D., Zhang, H., and Zijlstra, M · 2022
Cited alongside, same era.
Laidlaw, C. and Dragan, A · 2022
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Improving intrinsic exploration with language abstractions, 2022
Mu, J., Zhong, V., Raileanu, R., Jiang, M., Goodman, N., Rocktäschel, T., and Grefenstette, E · 2022
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Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Collaborating with humans without human data, 2022
Strouse, D., McKee, K. R., Botvinick, M., Hughes, E., and Everett, R · 2022
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Semantic exploration from language abstractions and pretrained representations, 2022
Tam, A. C., Rabinowitz, N. C., Lampinen, A. K., Roy, N. A., Chan, S. C. Y., Strouse, D., Wang, J. X., Banino, A., and Hill, F · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E. H., Le, Q., and Zhou, D · 2022
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Adversarial diversity in hanabi
Cui, B., Lupu, A., Sokota, S., Hu, H., Wu, D. J., and Foerster, J. N · 2023
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Reward design with language models
Kwon, M. and Sadigh, D · 2023
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