Required tools for playing around with memory:
hexdump
objdump
readelf
xxd
gcore
[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 |
""" 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 |
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
: discountlam=0.97
: see GAE paper for explanationagent=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 activationscg_damping
: multiple of the identity added for conjugate gradient// 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 | |
// |
# 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 |