ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.
1. The document discusses implicit behavioral cloning, which was presented in a 2021 Conference on Robot Learning (CoRL) paper.
2. Implicit behavioral cloning uses an implicit model rather than an explicit model to map observations to actions. The implicit model is trained using an InfoNCE loss function to discriminate positive observation-action pairs from negatively sampled pairs.
3. Experiments showed that the implicit model outperformed explicit models on several manipulation tasks like bi-manual sweeping, insertion, and sorting. The implicit approach was able to generalize better than explicit behavioral cloning.
The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.
1. The document discusses implicit behavioral cloning, which was presented in a 2021 Conference on Robot Learning (CoRL) paper.
2. Implicit behavioral cloning uses an implicit model rather than an explicit model to map observations to actions. The implicit model is trained using an InfoNCE loss function to discriminate positive observation-action pairs from negatively sampled pairs.
3. Experiments showed that the implicit model outperformed explicit models on several manipulation tasks like bi-manual sweeping, insertion, and sorting. The implicit approach was able to generalize better than explicit behavioral cloning.
This document is a slide of the "2019 ultra-fast learning for the future! Fluid analysis tool box OpenFOAM" presented at the 71st Open CAE Local User Group @ Kansai.
Customization of LES turbulence model in OpenFOAMmmer547
This slide is the distribution material on the seminar, "Customization of LES turbulence model in OpenFOAM". (June 13 2015 "OpenCAE Local User Group @ Kansai")
http://ofbkansai.sakura.ne.jp/
This document is the distribution material on "Code-Saturne beginner seminar". (November 1 2014 "OpenCAE Study Meeting @ Kansai")
http://ofbkansai.sakura.ne.jp/