karpathy

# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {

ynott

1. Environmental information

• OS: Ubuntu 20.04.2 LTS (GNU/Linux 5.8.0-59-generic x86_64)
• Network: 192.168.xxx.0/24
• Ubuntu multipass host machine IP: 192.168.xxx.yyy(static IP)
• NIC: enp2s0(bridge host NIC)
• Bridge NIC:br0

2. Prerequisites

elumixor

import gym
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.optim import Adam
from torch.distributions import Categorical
from collections import namedtuple

env = gym.make('CartPole-v0')

mda590

"""
Produces load on all available CPU cores.
Requires system environment var STRESS_MINS to be set.
"""

from multiprocessing import Pool
from multiprocessing import cpu_count
import time
import os

allenyllee

#!/bin/bash

##/*
## * @Author: AllenYL 
## * @Date: 2017-11-08 11:37:31 
## * @Last Modified by:   [email protected] 
## * @Last Modified time: 2017-11-08 11:37:31 
## */

#

michaellihs

tmux Cheat Sheet

Table of Contents

• General Usage
• Shortcuts
• Commands
• Scripting tmux
• Configuring tmux

kashif

Cross Entropy Method

How do we solve for the policy optimization problem which is to maximize the total reward given some parametrized policy?

Discounted future reward

To begin with, for an episode the total reward is the sum of all the rewards. If our environment is stochastic, we can never be sure if we will get the same rewards the next time we perform the same actions. Thus the more we go into the future the more the total future reward may diverge. So for that reason it is common to use the discounted future reward where the parameter discount is called the discount factor and is between 0 and 1.

A good strategy for an agent would be to always choose an action that maximizes the (discounted) future reward. In other words we want to maximize the expected reward per episode.

simme

# First install tmux
brew install tmux

# For mouse support (for switching panes and windows)
# Only needed if you are using Terminal.app (iTerm has mouse support)
Install http://www.culater.net/software/SIMBL/SIMBL.php
Then install https://bitheap.org/mouseterm/

# More on mouse support http://floriancrouzat.net/2010/07/run-tmux-with-mouse-support-in-mac-os-x-terminal-app/