oskn-fish Goto Github PK

a0c

Code for the paper Alpha Zero in Continuous Action Space (A0C) (https://arxiv.org/pdf/1805.09613.pdf)

alpha-zero-general

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

alphazero-gym

AlphaZero for continuous control tasks

boltzmann_distoribution

collatz_trajectory_plot

deepl_pdf

escalation

An R package providing a unified interface to several dose-finding trial methodologies

facefusion

Next generation face swapper and enhancer

facefusion-sagemaker-studio-lab

Installer for Facefusion on Sagemaker Studio Lab

index_pdf_translation

inforetriever

kalmanfilter

カルマンフィルタの実装

life_game

logistic_simulation

marp-theme-academic

Academic Presentation Slides with Marp

mcts-general

General Python implementation of Monte Carlo Tree Search for the use with Open AI Gym environments.

mcts_oncology

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

notes

ノート

oj

Tools for various online judges. Downloading sample cases, generating additional test cases, testing your code, and submitting it.

optimal_adaptive_allocation_in_a_dose-response_study

Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad El Hanafi, Takashi Sozu, Kentaro Sakamaki "Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study" Statistics in Medicine 2021

optimal_dose_escalation_in_phase_i_oncology_trials

Supplementary Materials for Kentaro Matsuura, Kentaro Sakamaki, Junya Honda, Takashi Sozu "Optimal Dose Escalation Methods using Deep Reinforcement Learning in Phase I Oncology Trials" Journal of Biopharmaceutical Statistics 2022

pdf_to_text

pure_search

puyopuyo_rl

python_gomi_files

pytorch_tutorials_jp

PyTorch公式チュートリアル（日本語翻訳版）の各ノートブックファイル（Google Colab用）です

rar_to_pdf

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.

rllib_tutorial