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This project was completed on October 15, 2015. It's a software package that consists of a set of AI agents for RPSLW (a stochastic game). One of the agents was implemented, so that it could learn how to play against different types of players on its own using Reinforcement Learning (specifically, using model-free type of RL - Q learning). To l…

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bnurbekov/AI_Agents_For_Rock_Paper_Scissors

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AI_Final_Project

Project structure

RPS.py

The main script that executes game logic.

Player.py

Contains implementation for different players.

Running the code

./RPS.py --player1 playerNumber|h --player2 playerNumber|h --numberOfGames number

*Note: options should be necessarily provided

Option description

player1

Specifies the type of the player for player one. Should be number 0-8 or h (for human player).

player2

Specifies the type of the player for player two. Should be number 0-8 or h (for human player).

numberOfGames

Specifies how many games will be played.

Output

The RPS script outputs three types of logs (as files): generic log, log for player 1, log for player 2. In general logs contain statistics for players and the game in general.

About

This project was completed on October 15, 2015. It's a software package that consists of a set of AI agents for RPSLW (a stochastic game). One of the agents was implemented, so that it could learn how to play against different types of players on its own using Reinforcement Learning (specifically, using model-free type of RL - Q learning). To l…

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