Find the best League of Legends Miss Fortune duos guide. Top, jungle, mid, bot, support roles on ranked solo/duo/flex, aram, normal blind/draft. S15 Patch 15.4.

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2020年10月19日 · off-policy的根本原因是在计算Q值的时候，不再需要整个动作序列了，on-policy策略使用整个动作序列的reward累加或者带有折扣的reward累加来确定Q值，从而帮助选择策略。 off-policy利用了TD-learning的思想，使用 神经网络 来估计Return，Q值计算只需要当前奖励和之后的估计Q值。 这样不需要记录完整序列，只需要记录状态、动作、奖励即可。 所以我们可以将使用其他episode采样的数据来训练当前的policy。 坏处就是又引入了误差（因为多了一处 …

2021年5月18日 · To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence.

actor-critic是为了解决两个问题：基于值的算法 (例如Q-learning)的高偏差 (high bias)和基于策略梯度的算法 (例如使用蒙特卡洛算法优化的策略梯度)的高方差 (high varience)。 关于这两个问题的解释可以参考 强化学习中值函数与优势函数的估计方法 - 知乎 (zhihu.com) 中的描述。 简而言之，Actor critic不再估计一个动作的绝对价值奖励 (例如上面提到的reward-to-go的绝对奖励)，而是相对与其他动作的优势： A^ {\pi} (s,a) = Q^ {\pi} (s, a) - V^ {\pi} (s) 参考PPO论文中对优势 A 的 …

We investigate the conformation of poly (2,6-diphenyl- p -phenylene oxide) (PPPO) in good and mixed solvents by small-angle neutron scattering (SANS) across its ternary phase diagram. Dichloromethane was selected as a “good” solvent and heptane as a “poor” solvent whose addition eventually induces demixing and polymer precipitation.

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture. We prove that MF-PPO attains the globally …

2022年1月28日 · To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence.

2021年5月18日 · To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural...

2021年5月18日 · To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence.