Game

learning git branch

Git Merge

• Git Merge
  • Solve conflict
    • Tools
    • Commands

Solve conflict

Tools

In nvim, I use plugin fugitive

Commands

open two vertical buffer (one form target, another from feature)

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:Gvdiffsplit!

stay on middle and use

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:diffget [buffername](local)

OR
go to target/feature buffer and use

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:diffput [buffername](target/feature)

save the local file and use

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:Git add .
:Git commit -m "solve merge conflicts"

• Add remote repository
• Branch

Add remote repository

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git remote add origin git@github.com:...

origin is the name of the repository
Check remote repository settings:

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git remote -v

Before push to remote repository, make sure the branch name is the same.

Branch

find all the branch

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git branch -a

find all the branch on remote repository

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git remote -r 

Rename

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git branch -m oldBranchName newBranchName

Move to branch:

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git checkout [branchname/SHA-1]

What is a Git submodule?

A Git submodule allows a user to include a Git repository as a subdirectory of another Git repository. This can be useful when a project needs to include and use another project. For example, it may be a third-party library or a library developed independently for use in multiple parent projects. With submodules, these libraries can be managed as independent projects while being used in the user’s project. This allows for better organization and management of the code.

How to add a submodule to a project

To add an existing Git repository as a submodule to a project, the git submodule add command can be used. The command format is git submodule add <url> <path>, where <url> is the URL of the submodule repository and <path> is the storage path of the submodule in the project. For example, if a user wants to add the remote repository https://github.com/username/repo.git as a submodule to their project and store it in the my-submodule directory, they can use the following command:

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git submodule add https://github.com/username/repo.git my-submodule

This will successfully add a submodule named my-submodule to the user’s project.

• check commit history
• change version
• switch to latest
• need a early version of a repository
  • Fork the latest repository on Github
  • clone the forked repository
  • change the version locally and push

check commit history

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git log
git log --pretty=oneline

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commit 0637340f4d8d288a8d24dd5193855c9e5def776d (HEAD -> main, origin/main, origin/HEAD)
Author: Chen-Yulin <chenyulin@sjtu.edu.cn>
Date:   Tue Mar 21 20:55:16 2023 +0800

    modify obsidian dir

commit dde357abf53f26972bd3ad0e1b1e840412b26fc7
Author: Chen-Yulin <chenyulin@sjtu.edu.cn>
Date:   Tue Mar 21 20:54:57 2023 +0800

    modify obsidian dir

commit 089578f74d206c7fbb503a293d34edf878dad3f3
Author: Chen-Yulin <chenyulin@sjtu.edu.cn>
Date:   Tue Mar 21 20:34:09 2023 +0800

    fix plugin compatibility

commit b62c94796b757851a5e8f38925f28b160a8bf4f0
Author: Chen-Yulin <chenyulin@sjtu.edu.cn>
Date:   Tue Mar 21 14:59:02 2023 +0800

change version

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git reset --hard [version hash]

switch to latest

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git pull

or locally

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git checkout master

need a early version of a repository

Fork the latest repository on Github

clone the forked repository

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git clone ...

change the version locally and push

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git log
...
git reset --hard [version hash]
git push --force

Maintain my website

link
ALWAYS

Write a script that can transfer my obsidian note to website

DONE!

Create a game, namely Besiege2.0

WORKING~

Materials

compilations

Dataset

H3.6m

Human3.6M

Parse

Paper

Depth camera

Doc

Human motion prediction using recurrent neural networks

Repository
Paper

Input

raw data : for each subjects(S1,S2 …) , each action(walking, waiting, smoking …), each sub sequence(1/2):
$(n) \times 99$ (np.ndarray, float32)

From data_utils.load_data() used by translate.read_all_data()

train data: the composed dictionary ((suject_id, action, subaction_id, ‘even’) as key) of raw data (just even rows), with one hot encoding columns for action type, if action is specified (normal case), just append an all 1 column to rawdata. Size of each dictionary value:
$(n/2) \times (99 + actions;count)$

complete data: all data joint together, from different subjects, actions, sub sequences:
$(n) \times 99$

From translate.read_all_data() used by translate.train()

train set : normalized train data, throw out data with $std < 1e-4$ (accroding to complete data). Size of each dictionary value:
$(n/2) \times ((99-used;dimension;count) + actions;count)$

Human Dimension

After the analyzztion of the complete data, human dimension has been fixed to $54$.

From Seq2SeqModel.get_batch() used by translate.train()

total_seq: $60$ ($[0,59]$)

source_seq_len: $50$
target_seq_len: $10$

batch_size: $16$

encoder_inputs: $16\times 49\times (54+actions;count)$
Interpretation: [batch,frame,dimension]
frame range: $[0,48]$

decoder_inputs: $16\times 10\times (54+actions;count)$
frame range: $[49,58]$

decoder_outputs: $16\times 10\times (54+actions;count)$
frame range: $[50,59]$

Model prediction input

encoder_inputs: Tensor form of encoder_inputs from Seq2SeqModel.get_batch()

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torch.from_numpy(encoder_inputs).float()

decoder_inputs: Tensor form of decoder_inputs from Seq2SeqModel.get_batch()

Example

For detailed usage, please see [Adopted] human-motion-prediction-pytorch\src\predict.ipynb

Reminder

The kinect camera’s output is not guaranteed to be consistent with the input of this model (some features are cut off), so further research is needed.

Pykinect

Run pyKinectAzure\examples\exampleBodyTrackingTransformationComparison to get the camera output record in pyKinectAzure\saved_data, saved as .npy

• Reference
• Types
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
    • Action space
    • Policy
    • Episode
    • Horizon
    • Return
    • Value function
    • Q function

Reference

Deep-Reinforcement-Learning-With-Python

Types

Supervised learning

In supervised learning, the machine learns from training data. The training data consists of a labeled pair of inputs and outputs. So, we train the model (agent) using the training data in such a way that the model can generalize its learning to new unseen data. It is called supervised learning because the training data acts as a supervisor, since it has a labeled pair of inputs and outputs, and it guides the model in learning the given task.

Regression

Quantitative response
predict a quantitative variable from a set of features

Classification

Categorical response
predict a categorical variable

Unsupervised learning

Similar to supervised learning, in unsupervised learning, we train the model (agent) based on the training data. But in the case of unsupervised learning, the training data does not contain any labels; that is, it consists of only inputs and not outputs. The goal of unsupervised learning is to determine hidden patterns in the input. There is a common misconception that RL is a kind of unsupervised learning, but it is not. In unsupervised learning, the model learns the hidden structure, whereas, in RL, the model learns by maximizing the reward.

Reinforcement learning

Action space

The set of all possible actions in the environment is called the action space. Thus, for this grid world environment, the action space will be [up, down, left, right]. We can categorize action spaces into two types:

• Discrete action space
  When our action space consists of actions that are discrete, then it is called a discrete action space. For instance, in the grid world environment, our action space consists of four discrete actions, which are up, down, left, right, and so it is called a discrete action space.
• Continuous action space
  When our action space consists of actions that are continuous, then it is called a continuous action space. For instance, let’s suppose we are training an agent to drive a car, then our action space will consist of several actions that have continuous values, such as the speed at which we need to drive the car, the number of degrees we need to rotate the wheel, and so on. In cases where our action space consists of actions that are continuous, it is called a continuous action space.

Policy

A policy defines the agent’s behavior in an environment. The policy tells the agent what action to perform in each state.
Over a series of iterations, the agent will learn a good policy that gives a positive reward.
The optimal policy tells the agent to perform the correct action in each state so that the agent can receive a good reward.

• Deterministic Policy
  deterministic policy tells the agent to perform a one particular action in a state. Thus, the deterministic policy maps the state to one particular action

• Stochastic Policy
  maps the state to a probability distribution over an action space.

  • Categorical policy
    when the action space is discrete
    uses categorical probability distribution over action space to select actions
  • Gaussian policy
    when our action space is continuous
    the stochastic policy uses Gaussian probability distribution over action space to select actions when the action space is continuous

Episode

The agent interacts with the environment by performing some action starting from the initial state and reach the final state. This agent-environment interaction starting from the initial state until the final state is called an episode. For instance, in the car racing video game, the agent plays the game by starting from the initial state (starting point of the race) and reach the final state (endpoint of the race). This is considered an episode. An episode is also often called trajectory (path taken by the agent)

• Episodic task
  As the name suggests episodic task is the one that has the terminal state. That is, episodic tasks are basically tasks made up of episodes and thus they have a terminal state. Example: Car racing game.
• Continuous task
  Unlike episodic tasks, continuous tasks do not contain any episodes and so they don’t have any terminal state. For example, a personal assistance robot does not have a terminal state.

Horizon

Horizon is the time step until which the agent interacts with the environment. We can classify the horizon into two:

• Finite horizon
  If the agent environment interaction stops at a particular time step then it is called finite Horizon. For instance, in the episodic tasks agent interacts with the environment starting from the initial state at time step t =0 and reach the final state at a time step T. Since the agent environment interaction stops at the time step T, it is considered a finite horizon.
• Infinite horizon
  If the agent environment interaction never stops then it is called an infinite horizon. For instance, we learned that the continuous task does not have any terminal states, so the agent environment interaction will never stop in the continuous task and so it is considered an infinite horizon.

Return

Return is the sum of rewards received by the agent in an episode.

Value function

Value function or the value of the state is the expected return that the agent would get starting from the state $s$ following the policy $\pi$

Q function

implies the expected return agent would obtain starting from the state $s$ and an action $a$ following the policy $\pi$.

Book

Matrix Cook Book

Relationship

Prime theory: An integrated view of motivation and emotion

Motivation

what is my motivation
Intrinsic Motivation and norms about payment

Emotion

what is my emotion
In old days people have high spirits

the obs dictionary’s builder

Report

For me, intrinsic and extrinsic motivation differs a lot. Since my early age, I distinguish clearly between schoolwork and extracurricular activities. During week days, I devote 100% of my energy into schoolwork and during weekends, I participate in robot and model aircraft clubs.
Before I entered college, I think that completing coursework is a student’s duty and it is also very rewarding. By behaving well in school, I can receive praise from my parents, respectation from peers, promising jobs and For this reason, completing schoolwork is mainly motivated by extrinsic rewards.

My dad used to have an argue with my mum about whether they should reward me by giving me some money to buy what I want if I perform well in exams. He thought exam scores should not be related with money rewards. While my mum thought it is reasonable since rewards can motivate me a lot. After I entered college, I thought my dad was right, although money rewards did motivate me to get higher scores, it is extrinsic motivation. According to the textbook, extrinsic rewards can reduce intrinsic motivation. When I look back, I cannot clearly figure out what is my intrinsic motivation of studying hard at school. Most of the time, I am motivated to study a subject just for the reason that I am good at it. If I am not good at a subject, such as Chinese, I just keep persuding me that if my Chinese is not learnt well, I will not be able to enter a good school. Usually, during this process, I suffer from heavy depression and anxiety. Speaking of how much passion I have in all of these subjects, I have to say, very little. My behaviour in exam-oriented education is like a machine. I get rewards, commitments as inputs and output good scores.

Although I behaves like the walking dead in schoolwork, I have intrinsic motivations in other fields, such as building robots and flying model aircraft. These activities fulfill no obvious purpose other than enjoyment.