The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
This document summarizes a presentation on offline reinforcement learning. It discusses how offline RL can learn from fixed datasets without further interaction with the environment, which allows for fully off-policy learning. However, offline RL faces challenges from distribution shift between the behavior policy that generated the data and the learned target policy. The document reviews several offline policy evaluation, policy gradient, and deep deterministic policy gradient methods, and also discusses using uncertainty and constraints to address distribution shift in offline deep reinforcement learning.
The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
This document summarizes a presentation on offline reinforcement learning. It discusses how offline RL can learn from fixed datasets without further interaction with the environment, which allows for fully off-policy learning. However, offline RL faces challenges from distribution shift between the behavior policy that generated the data and the learned target policy. The document reviews several offline policy evaluation, policy gradient, and deep deterministic policy gradient methods, and also discusses using uncertainty and constraints to address distribution shift in offline deep reinforcement learning.
16. 量子数と電子構造
原子核
φ
θ
r
電 子 (r, θ, φ)
x
y
z
水素原子の電子について球面座標で
シュレーディンガー方程式を解くと,
主量子数(n)… 電子が原子核からどの程度離れているか?
方位量子数(l)… 軌道はどんな形をしているか?
磁気量子数(m)… 軌道(ローブ)がどの向きに伸びているか?
x
z
y
3dxy
x
y
z
2py
x
y
z
1s
スピン量子数(s)… 電子の自転の向きに相当
En = −hcR/n2
エネルギー準位