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StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.
Peng, P., Wen, Y., Yang, Y., Yuan, Q., Tang, Z., Long, H., and Wang, J · 2017
Earlier work this paper cites.
Starcraft ii: A new challenge for reinforcement learning
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., Schrittwieser, J., et al · 2017
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
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Textworld: A learning environment for text-based games
Côté, M.-A., Kádár, A., Yuan, X., Kybartas, B., Barnes, T., Fine, E., Moore, J., Hausknecht, M., El Asri, L., Adada, M., et al · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al · 2019
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Han, L., Xiong, J., Sun, P., Sun, X., Fang, M., Guo, Q., Chen, Q., Shi, T., Yu, H., Wu, X., et al · 2020
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Alfworld: Aligning text and embodied environments for interactive learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M · 2020
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Starcraft ii unplugged: Large scale offline reinforcement learning
Mathieu, M., Ozair, S., Srinivasan, S., Gulcehre, C., Zhang, S., Jiang, R., Le Paine, T., Zolna, K., Powell, R., Schrittwieser, J., et al · 2021
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Scc: An efficient deep reinforcement learning agent mastering the game of starcraft ii
Wang, X., Song, J., Qi, P., Peng, P., Tang, Z., Zhang, W., Li, W., Pi, X., He, J., Gao, C., et al · 2021
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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
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On efficient reinforcement learning for full-length game of starcraft ii
Liu, R.-Z., Pang, Z.-J., Meng, Z.-Y., Wang, W., Yu, Y., and Lu, T · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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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
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React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
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Glm-130b: An open bilingual pre-trained model, 2022
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., Tam, W. L., Ma, Z., Xue, Y., Zhai, J., Chen, W., Zhang, P., Dong, Y., and Tang, J · 2022
Cited alongside, same era.
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al · 2023
Agentbench: Evaluating llms as agents
Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., Gu, Y., Ding, H., Men, K., Yang, K., et al · 2023
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Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Introspective tips: Large language model for in-context decision making
Chen, L., Wang, L., Dong, H., Du, Y., Yan, J., Yang, F., Li, S., Zhao, P., Qin, S., Rajmohan, S., et al · 2023
Cited alongside, same era.
Mindagent: Emergent gaming interaction
Gong, R., Huang, Q., Ma, X., Vo, H., Durante, Z., Noda, Y., Zheng, Z., Zhu, S.-C., Terzopoulos, D., Fei-Fei, L., et al · 2023
Cited alongside, same era.
Metagpt: Meta programming for multi-agent collaborative framework
Hong, S., Zheng, X., Chen, J., Cheng, Y., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., et al · 2023
Cited alongside, same era.
A robust and opponent-aware league training method for starcraft ii
Huang, R., Wu, X., Yu, H., Fan, Z., Fu, H., FU, Q., and Wei, Y · 2023
Cited alongside, same era.
Camel: Communicative agents for" mind" exploration of large scale language model society
Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., and Ghanem, B · 2023
Cited alongside, same era.
Efficient reinforcement learning for starcraft by abstract forward models and transfer learning
Liu, R.-Z., Guo, H., Ji, X., Yu, Y., Pang, Z.-J., Xiao, Z., Wu, Y., and Lu, T
Cited in the paper.
An introduction of mini-alphastar
Liu, R.-Z., Wang, W., Shen, Y., Li, Z., Yu, Y., and Lu, T
Cited in the paper.
Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., and Anandkumar, A · 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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Proagent: Building proactive cooperative ai with large language models
Zhang, C., Yang, K., Hu, S., Wang, Z., Li, G., Sun, Y., Zhang, C., Zhang, Z., Liu, A., Zhu, S.-C., et al · 2023
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Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., Huang, G., Li, B., Lu, L., Wang, X., et al · 2023
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Learning to discuss strategically: A case study on one night ultimate werewolf
Jin, X., Wang, Z., Du, Y., Fang, M., Zhang, H., and Wang, J · 2024
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Token-level direct preference optimization
Zeng, Y., Liu, G., Ma, W., Yang, N., Zhang, H., and Wang, J · 2024
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