{"id":770,"date":"2023-10-28T12:48:27","date_gmt":"2023-10-28T12:48:27","guid":{"rendered":"https:\/\/todaysainews.com\/index.php\/2023\/10\/28\/building-interactive-agents-in-video-game-worlds-2\/"},"modified":"2025-04-27T07:32:34","modified_gmt":"2025-04-27T07:32:34","slug":"building-interactive-agents-in-video-game-worlds-2","status":"publish","type":"post","link":"https:\/\/todaysainews.com\/index.php\/2023\/10\/28\/building-interactive-agents-in-video-game-worlds-2\/","title":{"rendered":"Building interactive agents in video game worlds"},"content":{"rendered":"

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Notes<\/h2>\n

[1] Abramson, J., Ahuja, A., Barr, I., Brussee, A., Carnevale, F., Cassin, M., Chhaparia, R., Clark, S., Damoc, B., Dudzik, A. and Georgiev, P., 2020. Imitating interactive intelligence. arXiv preprint arXiv:2012.05672<\/i>.<\/p>\n

[2] Abramson, J., Ahuja, A., Brussee, A., Carnevale, F., Cassin, M., Fischer, F., Georgiev, P., Goldin, A., Harley, T. and Hill, F., 2021. Creating multimodal interactive agents with imitation and self-supervised learning. arXiv preprint arXiv:2112.03763<\/i>.<\/p>\n

[3] Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., Lillicrap, T., Muldal, A., Richards, B. and Santoro, A., 2022. Evaluating Multimodal Interactive Agents. arXiv preprint arXiv:2205.13274<\/i>.<\/p>\n

[4] Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T. and Joseph, N., 2022. Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv preprint arXiv:2204.05862<\/i>.<\/p>\n

[5] Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S. and Amodei, D., 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems<\/i>, 30<\/i>.<\/p>\n<\/p><\/div>\n

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