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Commit b1835bf

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Merge branch 'avinashkranjan:master' into master
2 parents fdc6be7 + 08f346f commit b1835bf

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‎.github/ISSUE_TEMPLATE/bug_report.md

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‎.github/ISSUE_TEMPLATE/bug_report.yml

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name: 🐛 Bug Report
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description: Report a bug report
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title: '[Bug]: '
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labels: ['🔧 goal: fix']
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body:
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- type: textarea
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id: what-happened
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attributes:
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label: Describe the bug
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placeholder: A clear and concise description of what the bug is.
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validations:
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required: true
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- type: textarea
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id: steps-to-reproduce
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attributes:
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label: Steps to reproduce the behavior
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placeholder: |
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1. Go to '...'
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2. Click on '....'
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3. Scroll down to '....'
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4. See error
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validations:
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required: false
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- type: textarea
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id: expected-behaviour
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attributes:
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label: Expected behavior
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placeholder: A clear and concise description of what you expected to happen.
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validations:
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required: true
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- type: textarea
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id: screenshots
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attributes:
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label: Screenshots
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placeholder: If applicable, add screenshots to help explain your problem.
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validations:
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required: false
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- type: dropdown
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id: device
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attributes:
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label: Device?
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multiple: true
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options:
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- Desktop
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- Mobile
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- type: dropdown
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id: operating-system
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attributes:
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label: Which OS are affected?
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multiple: true
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options:
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- Mac
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- Windows
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- Linux
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- type: textarea
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id: version
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attributes:
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label: Enter the version of your web browser
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placeholder: Version
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validations:
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required: true
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- type: dropdown
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id: browsers
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attributes:
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label: Which browsers are affected?
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multiple: true
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options:
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- Firefox
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- Chrome
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- Safari
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- Microsoft Edge
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- Brave
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- Other
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- type: textarea
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id: additional-info
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attributes:
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label: Additional context
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placeholder: Add any other context about the problem here.
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validations:
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required: false
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- type: checkboxes
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id: terms
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attributes:
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label: 'Record'
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options:
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- label: I agree to follow this project's Code of Conduct
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required: true
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- label: I'm a GSSoC'23 contributor
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- label: I want to work on this issue

‎.github/ISSUE_TEMPLATE/feature_request.md

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name: ✨ Feature Request
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description: Suggest a feature request
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title: '[Feat]: '
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labels: ['⭐ goal: addition']
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body:
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- type: textarea
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id: what-feature
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attributes:
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label: Is your feature request related to a problem? Please describe.
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placeholder: A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
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validations:
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required: true
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- type: textarea
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id: solution
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attributes:
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label: Describe the solution you'd like.
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placeholder: A clear and concise description of what you want to happen.
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validations:
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required: true
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- type: textarea
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id: alternatives
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attributes:
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label: Describe alternatives you've considered.
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placeholder: A clear and concise description of any alternative solutions or features you've considered.
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validations:
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required: true
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- type: textarea
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id: screenshots
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attributes:
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label: Show us the magic with screenshots
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placeholder: Attach screenshots to visualize your idea
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validations:
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required: false
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- type: checkboxes
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id: terms
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attributes:
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label: 'Record'
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options:
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- label: I agree to follow this project's Code of Conduct
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required: true
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- label: I'm a GSSoC'23 contributor
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- label: I want to work on this issue
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‎.github/ISSUE_TEMPLATE/script-addition.md

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name: ✨ Script Addition
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description: Describe this Script you want to add
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title: '[Script]: '
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labels: ['⭐ goal: Script-addition']
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body:
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- type: textarea
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id: Aim
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attributes:
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label: Aim
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placeholder: What is the objective of the Script
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validations:
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required: true
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- type: textarea
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id: Details
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attributes:
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label: Details
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placeholder: What the features will your script have
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validations:
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required: true
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- type: checkboxes
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id: terms
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attributes:
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label: 'Record'
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options:
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- label: I agree to follow this project's Code of Conduct
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required: true
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- label: I'm a GSSoC'23 contributor
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- label: I want to work on this issue
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‎.github/workflows/py_lint.yml

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# Checkout the code base #
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##########################
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- name: Checkout Code
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uses: actions/checkout@v2
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uses: actions/checkout@v3
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with:
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# Full git history is needed to get a proper list of changed files within `super-linter`
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fetch-depth: 0
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# Run Linter against code base #
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################################
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- name: Lint Code Base
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uses: github/super-linter@v3
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uses: github/super-linter@v5
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env:
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VALIDATE_ALL_CODEBASE: false
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VALIDATE_PYTHON: true
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Jun 4 18:02:01 2021
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@author: Ayush
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"""
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import gym
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import numpy as np
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from IPython.display import clear_output
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env = gym.make('Taxi-v3')
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episodes = 10
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for episode in range(1,episodes):
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state = env.reset()
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done= False
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score=0
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while not done:
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env.render()
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state,reward,done,info = env.step(env.action_space.sample())
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score += reward
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clear_output(wait=True)
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print('Episode: {}\nScore: {}'.format(episode, score))
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env.close()
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#Creating Q-Table
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actions = env.action_space.n
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state = env.observation_space.n
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q_table = np.zeros((state,actions))
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#q_table.shape
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#q_table
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#Parameters for Q-Learning
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num_episodes = 10000
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max_steps_per_episode =1000
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learning_rate=0.01
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discount_rate = 0.99
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exploration_rate=1
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max_exploration_rate = 1
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min_exploration_rate = 0.01
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exploration_decay_rate= 0.01
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rewards_all_episodes = []
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#Q-Learning Algorithm
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import random
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for episode in range(num_episodes):
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state = env.reset()
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done = False
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reward_current_episode = 0
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for step in range(max_steps_per_episode):
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#Exploration vs Exploitation trade-off
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exploration_threshold = random.uniform(0,1)
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if exploration_threshold > exploration_rate:
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action = np.argmax(q_table[state,:])
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else:
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action = env.action_space.sample()
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new_state,reward,done,info = env.step(action)
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#Update Q-Table
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q_table[state,action] = q_table[state,action]*(1-learning_rate)+ learning_rate*(reward + discount_rate * np.max(q_table[new_state, :]))
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state=new_state
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reward_current_episode += reward
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if done== True:
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break
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exploration_rate = min_exploration_rate + \
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(max_exploration_rate- min_exploration_rate) * np.exp(-exploration_decay_rate * episode)
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rewards_all_episodes.append(reward_current_episode)
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print("***** Training Finished *****")
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q_table
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#Calculate and print average reward per thousand episodes
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rewards_per_thousand_episodes = np.split(np.array(rewards_all_episodes), num_episodes/1000)
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count = 1000
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print("Average per thousand episodes")
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for r in rewards_per_thousand_episodes:
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print(count, ":", str(sum(r/1000)))
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count+=1000
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# Visualize Agent
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import time
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for episode in range(3):
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status = env.reset()
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done = False
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print("Episode is: "+ str(episode))
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time.sleep(1)
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for step in range(max_steps_per_episode):
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clear_output(wait=True)
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env.render()
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time.sleep(.4)
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action = np.argmax(q_table[state,:])
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new_state, reward, done, info = env.step(action)
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if done:
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clear_output(wait=True)
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env.render()
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if reward == 1:
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print("****Reached Goal****")
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time.sleep(2)
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clear_output(wait=True)
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else:
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print("****Failed****")
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time.sleep(2)
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clear_output(wait=True)
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break
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state=new_state
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env.close()
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