FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| from chainer import cuda | |
| import cupy as cp | |
| import time, threading | |
| #backend |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| suppressMessages(library("gbm")) | |
| suppressMessages(library("plotly")) | |
| PlotInteraction <- function(model, interactingVariables) { | |
| interactionEffect <- .ComputeInteractionEffect(model, interactingVariables) | |
| .PlotInteractionSurface(interactionEffect, interactingVariables) | |
| } | |
| .PlotInteractionSurface <- function(interactionEffect, interactingVariables) { | |
| xAxis <- .GetXAxis(interactionEffect) |
by Bjørn Friese
Beautiful is better than ugly. Explicit is better than implicit.
I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.
Code for Keras plays catch blog post
python qlearn.py
The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.
Unofficial documentation to internal Riot Games APIs for LOL eSports.
| set +x | |
| # Add this as the 1st build Exec shell | |
| # Only for situations where you have 1 and only 1 executor per machine | |
| # And you don't care about processes being left running after the job run ends | |
| # Look for processes that have a BUILD_ID env var | |
| # that is NOT the same as the current job's BUILD_ID | |
| # nor same as dontKillMe | |
| echo "Killing orphan spawned processes..." |