The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
KDD Cup 2021で開催された時系列異常検知コンペ
Multi-dataset Time Series Anomaly Detection (https://compete.hexagon-ml.com/practice/competition/39/) に参加して
5位入賞した解法の紹介と上位解法の整理のための資料です.
9/24のKDD2021参加報告&論文読み会 (https://connpass.com/event/223966/) の発表資料です.
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
1. The document discusses research on time-series big data feature extraction and real-time forecasting.
2. The research aims to predict the future by analyzing large-scale data in order to transform society through optimizing social activities in real-time.
3. Key areas of focus include tensor analysis of complex time-stamped event data, non-linear modeling of non-linear social phenomena in big data, and real-time processing.
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
KDD Cup 2021で開催された時系列異常検知コンペ
Multi-dataset Time Series Anomaly Detection (https://compete.hexagon-ml.com/practice/competition/39/) に参加して
5位入賞した解法の紹介と上位解法の整理のための資料です.
9/24のKDD2021参加報告&論文読み会 (https://connpass.com/event/223966/) の発表資料です.
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
1. The document discusses research on time-series big data feature extraction and real-time forecasting.
2. The research aims to predict the future by analyzing large-scale data in order to transform society through optimizing social activities in real-time.
3. Key areas of focus include tensor analysis of complex time-stamped event data, non-linear modeling of non-linear social phenomena in big data, and real-time processing.
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。