path: root/python-easy-qlearning.spec

%global _empty_manifest_terminate_build 0
Name:		python-Easy-QLearning
Version:	1.1.0
Release:	1
Summary:	Simplify the creation of QLearning
License:	MIT License
URL:		https://github.com/ProfesseurIssou/Easy-QLearning
Source0:	https://mirrors.aliyun.com/pypi/web/packages/34/38/b5321d647a5e20327b58e9da1fb353f3df455f49f0682004863b57a42c82/Easy-QLearning-1.1.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-pytest

%description
# EQL

Simplify the creation of QLearning


## Installation

Run the following to install:
```python
pip install Easy-QLearning
```


## Usage
```python
import EQL
####Initialise QTable####
#Mission: go to the house without run over the human
#Game grid
Grid = [#1 = House, -1 = Human
    [0,0,1],
    [0,-1,0],
    [0,0,0]
]

#First car position coordinates
x = 0
y = 2
#Current state
state = 7
#Action list and travel coordinate
actions = [
    [-1, 0], # Up
    [1, 0], #Down
    [0, -1], # Left
    [0, 1] # Right
]

#Make QTable
Qtable = EQL.QLearning(nbAction=4,nbState=10)

####Train the QTable####
#100 games
for _ in range(100):
    #Reset the car position
    x = 0
    y = 2
    #Default state
    state = 7
    #While the car are not in the house
    while x != 2 or y != 0:
        #Display the grid
        print("---------------------")
        yTemp = 0
        for line in Grid:
            xTemp = 0
            for pt in line:
                print("%s\t" % (pt if yTemp != y or xTemp != x else "X"), end="")
                xTemp += 1
            yTemp += 1
            print("")
        #Choose an action
        action = Qtable.takeAction(state,epsilon=0.4)
        #Move the car
        y = max(0, min(y + actions[action][0],2))
        x = max(0, min(x + actions[action][1],2))
        #Calcul the position in the grid (state)
        newState = (y*3+x+1)
        #Get the reward of the position
        reward = Grid[y][x]
        print("state : ", newState)
        print("reward : ", reward)
        #Update Q function
        Qtable.updateQFunction(newState,state,reward)
        #Next state
        state = newState
#Display the QTable
for s in range(0, 9):
    print(s, Qtable.QTable[s])

#Save my QTable in myTable.npz
Qtable.saveQTable("myTable")

#Load my QTable from myTable.npz
Qtable.loadQTable("myTable")
```


```bash
$ pip install -e .[dev]
```



%package -n python3-Easy-QLearning
Summary:	Simplify the creation of QLearning
Provides:	python-Easy-QLearning
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-Easy-QLearning
# EQL

Simplify the creation of QLearning


## Installation

Run the following to install:
```python
pip install Easy-QLearning
```


## Usage
```python
import EQL
####Initialise QTable####
#Mission: go to the house without run over the human
#Game grid
Grid = [#1 = House, -1 = Human
    [0,0,1],
    [0,-1,0],
    [0,0,0]
]

#First car position coordinates
x = 0
y = 2
#Current state
state = 7
#Action list and travel coordinate
actions = [
    [-1, 0], # Up
    [1, 0], #Down
    [0, -1], # Left
    [0, 1] # Right
]

#Make QTable
Qtable = EQL.QLearning(nbAction=4,nbState=10)

####Train the QTable####
#100 games
for _ in range(100):
    #Reset the car position
    x = 0
    y = 2
    #Default state
    state = 7
    #While the car are not in the house
    while x != 2 or y != 0:
        #Display the grid
        print("---------------------")
        yTemp = 0
        for line in Grid:
            xTemp = 0
            for pt in line:
                print("%s\t" % (pt if yTemp != y or xTemp != x else "X"), end="")
                xTemp += 1
            yTemp += 1
            print("")
        #Choose an action
        action = Qtable.takeAction(state,epsilon=0.4)
        #Move the car
        y = max(0, min(y + actions[action][0],2))
        x = max(0, min(x + actions[action][1],2))
        #Calcul the position in the grid (state)
        newState = (y*3+x+1)
        #Get the reward of the position
        reward = Grid[y][x]
        print("state : ", newState)
        print("reward : ", reward)
        #Update Q function
        Qtable.updateQFunction(newState,state,reward)
        #Next state
        state = newState
#Display the QTable
for s in range(0, 9):
    print(s, Qtable.QTable[s])

#Save my QTable in myTable.npz
Qtable.saveQTable("myTable")

#Load my QTable from myTable.npz
Qtable.loadQTable("myTable")
```


```bash
$ pip install -e .[dev]
```



%package help
Summary:	Development documents and examples for Easy-QLearning
Provides:	python3-Easy-QLearning-doc
%description help
# EQL

Simplify the creation of QLearning


## Installation

Run the following to install:
```python
pip install Easy-QLearning
```


## Usage
```python
import EQL
####Initialise QTable####
#Mission: go to the house without run over the human
#Game grid
Grid = [#1 = House, -1 = Human
    [0,0,1],
    [0,-1,0],
    [0,0,0]
]

#First car position coordinates
x = 0
y = 2
#Current state
state = 7
#Action list and travel coordinate
actions = [
    [-1, 0], # Up
    [1, 0], #Down
    [0, -1], # Left
    [0, 1] # Right
]

#Make QTable
Qtable = EQL.QLearning(nbAction=4,nbState=10)

####Train the QTable####
#100 games
for _ in range(100):
    #Reset the car position
    x = 0
    y = 2
    #Default state
    state = 7
    #While the car are not in the house
    while x != 2 or y != 0:
        #Display the grid
        print("---------------------")
        yTemp = 0
        for line in Grid:
            xTemp = 0
            for pt in line:
                print("%s\t" % (pt if yTemp != y or xTemp != x else "X"), end="")
                xTemp += 1
            yTemp += 1
            print("")
        #Choose an action
        action = Qtable.takeAction(state,epsilon=0.4)
        #Move the car
        y = max(0, min(y + actions[action][0],2))
        x = max(0, min(x + actions[action][1],2))
        #Calcul the position in the grid (state)
        newState = (y*3+x+1)
        #Get the reward of the position
        reward = Grid[y][x]
        print("state : ", newState)
        print("reward : ", reward)
        #Update Q function
        Qtable.updateQFunction(newState,state,reward)
        #Next state
        state = newState
#Display the QTable
for s in range(0, 9):
    print(s, Qtable.QTable[s])

#Save my QTable in myTable.npz
Qtable.saveQTable("myTable")

#Load my QTable from myTable.npz
Qtable.loadQTable("myTable")
```


```bash
$ pip install -e .[dev]
```



%prep
%autosetup -n Easy-QLearning-1.1.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-Easy-QLearning -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated