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tpokemon

https://github.com/kaitlynjpak/tpokemon

Science Score: 44.0%

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    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: kaitlynjpak
  • Language: Python
  • Default Branch: main
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Created 8 months ago · Last pushed 8 months ago
Metadata Files
Readme Citation

readme.txt 

Dependancies in terminal:
1. pip install pillow
2. pip install opencv-python

NOTE: You must NOT be in FULL SCREEN for certain game functions to work, please do not alter window size.

Description:
In this game, you are a pokemon trainer and your job is to explore the world map and find gyms to fight other pokemon. 
If you win the battle (by depleting the other pokemons health before yours is depleted), then you win the pokemon. 

There are 10 pokemon total, the default is your starter pokemon, and as you win games against other pokemon, 
those pokemon get added to the set of pokemon that the user can use in their next game. 
So at the beginning of each of the 9 battles in the game, you can select which pokemon you want to play in battle 
(the opponent is predetermined) and there's only 9 opponent pokemon (3 per difficulty level)). 

Use the 4 attack buttons available to your pokemon in this turn based game to deplete the health of your opponent before 
they can deplete yours! At the end of each battle your pokemons will automatically be healed so you can use them again if you wish!
Good Luck! 

Game toggles: 
'y' to battle
't' to view the open world map
'u' to input your face as your trainer sprite


Special Feature**: AI opponent pokemon

We used reinforcement learning to get the AI to play pokemon with very high accuracy by anticipating the user's next move. 
Then, we altered the algorithm in our game to purposely make wrong moves based on the difficulty level so the user would
win a certain percentage of the time based on the difficulty level of the gym.

**an additional special feature is our use of opencv to upload the user's photo to create a custom trainor!

Owner

  • Name: Kaitlyn Pak
  • Login: kaitlynjpak
  • Kind: user
  • Location: Pittsburgh, Pennsylvania
  • Company: Carnegie Mellon University

Citation (citations.txt) 

Below the dashed line, include easily understandable and verifiable 
citations to all the major sources you used for your project, as described
in the TP document: 
https://www.cs.cmu.edu/~112/notes/term-project-and-hack112.html#tp-policies

In addition, your code must also include citations directly in the code that 
make it clear where you use code that is partly or entirely not of your 
original design, and what the source is for that code.
------------------------------------>

ChatGPT was used for creating the damage and evasion functions in combat

Microsoft Copilot was used to create AI generated Welcome page

ChatGPT was used for filepath and qtables

ChatGPT was used to create assetsUtils and qtablesUtils files

ChatGPT was used for the getTile, drawTile functions and debugging some circular
import issues

https://medium.com/@nancy.q.zhou/teaching-an-ai-to-play-the-snake-game-using-reinforcement-learning-6d2a6e8f3b1c 
was also used for to outline what I needed for the RL files 
(more specifically, i used it to figure out what steps to take to make a reinforcement learning algorithm in python
for a turn based game since I planned to use qlearning).

used for qlearning:
1. https://www.youtube.com/watch?v=MI8ByADMh20 
2. https://www.youtube.com/watch?v=RiSCh4H-GY4 
3. https://www.youtube.com/watch?v=iKdlKYG78j4 to help understand and write the RL Q learning section
4. https://www.geeksforgeeks.org/q-learning-in-python/ to help flesh out the framework for q learning file

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