Merge branch 'master' into sciCal

Author: avinashkranjan

.github/workflows/py_lint.yml

@@ -36,7 +36,7 @@ jobs:
       # Checkout the code base #
       ##########################
       - name: Checkout Code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           # Full git history is needed to get a proper list of changed files within `super-linter`
           fetch-depth: 0
@@ -45,7 +45,7 @@ jobs:
       # Run Linter against code base #
       ################################
       - name: Lint Code Base
-        uses: github/super-linter@v3
+        uses: github/super-linter@v5
         env:
           VALIDATE_ALL_CODEBASE: false
           VALIDATE_PYTHON: true

Autonomous-Taxi-Agent/Autonomous TaxiAgent.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun  4 18:02:01 2021
+
+@author: Ayush
+"""
+
+import gym
+import numpy as np
+from IPython.display import clear_output
+
+env = gym.make('Taxi-v3')
+
+episodes = 10
+for episode in range(1,episodes):
+    state = env.reset()
+    done= False
+    score=0
+    
+    while not done:
+        env.render()
+        state,reward,done,info = env.step(env.action_space.sample())
+        score += reward
+        clear_output(wait=True)
+    print('Episode: {}\nScore: {}'.format(episode, score))
+env.close()
+
+#Creating Q-Table
+actions = env.action_space.n
+state = env.observation_space.n
+
+q_table = np.zeros((state,actions))
+
+#q_table.shape
+#q_table
+
+#Parameters for Q-Learning
+num_episodes = 10000
+max_steps_per_episode =1000
+learning_rate=0.01
+discount_rate = 0.99
+exploration_rate=1
+max_exploration_rate = 1
+min_exploration_rate = 0.01
+exploration_decay_rate= 0.01
+
+rewards_all_episodes = []
+
+#Q-Learning Algorithm
+import random
+for episode in range(num_episodes):
+    state = env.reset()
+    done = False
+    reward_current_episode = 0
+    
+    for step in range(max_steps_per_episode):
+        #Exploration vs Exploitation trade-off
+        exploration_threshold = random.uniform(0,1)
+        if exploration_threshold > exploration_rate:
+            action = np.argmax(q_table[state,:])
+        else:
+            action = env.action_space.sample()
+        new_state,reward,done,info = env.step(action)
+        
+        #Update Q-Table
+        q_table[state,action] = q_table[state,action]*(1-learning_rate)+ learning_rate*(reward + discount_rate * np.max(q_table[new_state, :]))
+        state=new_state
+        reward_current_episode += reward
+        
+        if done== True:
+            break
+    exploration_rate = min_exploration_rate + \
+        (max_exploration_rate- min_exploration_rate) * np.exp(-exploration_decay_rate * episode)
+    rewards_all_episodes.append(reward_current_episode)
+print("***** Training Finished *****")
+
+q_table
+
+#Calculate and print average reward per thousand episodes
+rewards_per_thousand_episodes = np.split(np.array(rewards_all_episodes), num_episodes/1000)
+count = 1000
+
+print("Average per thousand episodes")
+
+for r in rewards_per_thousand_episodes:
+    print(count, ":", str(sum(r/1000)))
+    count+=1000
+    
+# Visualize Agent
+import time 
+for episode in range(3):
+    status = env.reset()
+    done = False
+    print("Episode is: "+ str(episode))
+    time.sleep(1)
+    
+    for step in range(max_steps_per_episode):
+        clear_output(wait=True)
+        env.render()
+        time.sleep(.4)
+        
+        action = np.argmax(q_table[state,:])
+        
+        new_state, reward, done, info = env.step(action)
+        
+        if done:
+            clear_output(wait=True)
+            env.render()
+            if reward == 1:
+                print("****Reached Goal****")
+                time.sleep(2)
+                clear_output(wait=True)
+            else:
+                print("****Failed****")
+                time.sleep(2)
+                clear_output(wait=True)
+                
+            break
+        state=new_state
+env.close()
+
+
+

Autonomous-Taxi-Agent/README.md

@@ -0,0 +1,47 @@
+# Q-Learning with Taxi-v3 Environment
+
+This repository contains code for implementing the Q-Learning algorithm using the Taxi-v3 environment from the OpenAI Gym.
+
+## Prerequisites
+
+To run this code, you need the following dependencies:
+
+- Python 3.x
+- Gym: `pip install gym`
+- NumPy: `pip install numpy`
+
+## Getting Started
+
+1. Clone the repository: `git clone https://github.com/your_username/your_repository.git`
+2. Navigate to the cloned repository: `cd your_repository`
+
+## Running the Code
+
+1. Open the Python script `q_learning_taxi.py`.
+2. Configure the number of episodes, learning parameters, and other settings as needed.
+3. Run the script: `python q_learning_taxi.py`.
+
+## Understanding the Code
+
+The code performs the following steps:
+
+1. Imports the necessary libraries and initializes the Taxi-v3 environment.
+2. Runs a specified number of episodes, where each episode represents a learning iteration.
+3. Resets the environment for each episode and plays the game until completion.
+4. Renders the environment to visualize the game.
+5. Selects actions randomly for exploration or based on the learned Q-values for exploitation.
+6. Updates the Q-table based on the Q-Learning algorithm.
+7. Adjusts the exploration rate over time to balance exploration and exploitation.
+8. Stores the rewards obtained in each episode.
+9. Prints the Q-table after training.
+10. Calculates and prints the average reward per thousand episodes.
+11. Visualizes the agent's performance in a few test episodes.
+
+
+
+## Acknowledgments
+
+- [OpenAI Gym](https://gym.openai.com/)
+
+Feel free to modify and adapt this code according to your needs.
+

Background-Payload-Runner/readme.md

@@ -0,0 +1,15 @@
+# A PYTHON SCRIPT TO RUN PAYLOADS IN THE BACKGROUND
+
+
+Input:
+1. Which command line to use (command prompt or powershell)
+2. time delay after which payload will run
+3. Payload that is to be executed in the background
+
+
+
+The payload can be any payload
+>can be a harmless rickroll payload 
+>can be a payload that connects to a hacker's computer with reverse-shell
+
+Possibilities of the payload are endless , SKY IS THE LIMIT

Background-Payload-Runner/requirements.txt

@@ -0,0 +1 @@
+background==0.2.1

Background-Payload-Runner/run-payload.py

@@ -0,0 +1,33 @@
+import time
+import background
+import os
+
+##select which command line you want to run the payload on
+##commandline="cmd /c "
+commandline="powershell "
+
+time_delay=0 ##in seconds
+
+##CUSTOM PAYLOAD
+payload="Start-process chrome.exe https://www.youtube.com/watch?v=dQw4w9WgXcQ"  ##rickroll payload
+
+
+##other payloads (example)
+##payload="shutdown"
+##                        --shuts down the machine without any confirmation
+
+##payload="shutdown -force -restart"
+##                         --restarts the machine without any confirmation
+##payload="netsh wlan disconnect"
+##                         --disconnects the wifi of the machine
+
+##THESE ARE JUST A FEW. The possibilities are endless
+
+
+
+@background.task
+def work():
+    if(time_delay!=0):
+        time.sleep(time_delay)
+    os.system(commandline+payload)
+work()

Chess-Game/README.md

@@ -0,0 +1,40 @@
+# CHESS GAME
+
+_______________________________________________________________________
+
+## What is it
+
+- This program will provide the user an attractive user interface of Chess Game.
+- This chess game is created using PYTHON language.
+- Start the file on terminal.
+- File will automatically fetch the required files and create a interactive gui for the user.
+- User, now start to play.
+
+
+<br>
+
+## Modules Used
+
+- pygame
+
+Install this library using the below command:
+
+```pip install pygame```
+
+First install these libraries and use python 3 or above to enjoy this **Scientific Calculator**
+
+
+<br>
+
+## Screenshot 📸
+
+![Chess-Game](https://github.com/avinashkranjan/Amazing-Python-Scripts/assets/114330097/509af4eb-0f56-4af5-b898-331182889018)
+
+
+<br>
+
+## Creator 👦
+
+[Avdhesh Varshney](https://github.com/Avdhesh-Varshney)
+
+