karpathy

""" 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

joschu

Code used to obtain these results can be found at the url https://github.com/joschu/modular_rl, commit 50cdfdf375e69d86e3db6eb2ad0218ea6aebf371. The command line expression used for all the environments can be found in the text file below. Note that the same exact parameters and policies were used for all tasks, except for timesteps_per_batch, which was varied based on the difficulty of the task. The important parameters are:

• gamma=0.995: discount
• lam=0.97: see GAE paper for explanation
• agent=TrpoAgent: name of the class, which specifies policy and value function architecture. In this case, we used two hidden layers of size 64, with tanh activations
• cg_damping: multiple of the identity added for conjugate gradient

CMCDragonkai

Understanding the Memory Layout of Linux Executables

Required tools for playing around with memory:

• hexdump
• objdump
• readelf
• xxd
• gcore

ipedrazas

# knife cheat

## Search Examples

	knife search "name:ip*" 
	knife search "platform:ubuntu*"
	knife search "platform:*" -a macaddress
	knife search "platform:ubuntu*" -a uptime
	knife search "platform:ubuntu*" -a virtualization.system
	knife search "platform:ubuntu*" -a network.default_gateway

PhirePhly

// memdjpeg - A super simple example of how to decode a jpeg in memory
// Kenneth Finnegan, 2012
// blog.thelifeofkenneth.com
//
// After installing jpeglib, compile with:
// cc memdjpeg.c -ljpeg -o memdjpeg
//
// Run with:
// ./memdjpeg filename.jpg
//

pksunkara

[user]
	name = Pavan Kumar Sunkara
	email = pavan.sss1991@gmail.com
	username = pksunkara
[init]
	defaultBranch = master
[core]
	editor = nvim
	whitespace = fix,-indent-with-non-tab,trailing-space,cr-at-eol
	pager = delta