Allowing Humans and AI play together, unlike other environments such as gym. AI-Play allows the huamn or AI to play witn one another. AI-Play is an educational ,research framework for experimenting with AI agents.
To run please open your terminal and access the directory where you decided to place this repository on your disk and type the following command
python3 Play.py
if this doesn't work try
python Play.py
Note: you do need to have Python installed to run the script Play.py
root/
Players/
Here we find the player.py script which defines players abstract players and AI agents.
A human player is prompted for a unique name whereas the AI-player is not.
RLAgents
QPlayer: RL agent that uses tabular Q-Learning to perform updates, Inherits RLtabularagent and Player classes
DoubleQPlayer: RL agent that uses two Q functions to avoid over estimation of the Q-values (each Q function is chosen by probability .5)
NstepQAgent: RL agent that computes an N-step return (Gt) to compute the expected reward for Q(s,a) = Q(s,a) + alpha(Gt - Q(s,a)) Inherits: RLTabularAgent and Player
NstepDoubleAgent: RL agent that uses both N-step returns and Double Learning to update the Q-values
Inherits : RLTabularAgent and Player
SARSAgent: implementation of SARSA algorithm SARSA is an on-policy TD method that uses Q-values for to compute the policy Inherits: RLTabularAgent and Player
Environments the constructor of each environment takes two player objects and an integer for the number of episodes or games in which you wish to play
Currently only environment available are TicTacToe and Connect4 we plan to include other board-like games.
The Play script (Play.py) allows you (Human) to play with a QPlayer agent upon cloning this repository
To use AI-Play simply create your player objects and then pass them into the contrete TwoPlayerEnv class. Please keep in mind you will need to create a Python script to call the environment and the two player objects you wish to use for that environment.
You can change the types of players in each game by altering parameters for the environment
Credits Works Cited on the research conducted to create this project can be found at WorksCited.txt
Parameter Setting
If you wish to change the parameters of the agents consult the parameter guide (Parameter_Guide.txt) to understand the various settings and how they work