VarunRaval48 / checkers-AI

A checkers game which can be played against various Artificially Intelligent agents.

Which Python

This project is written using Python 3.6.4.

HOW TO RUN

For a list of available options, enter python checkers.py -h

By default, running python checkers.py will run a multiplayer checkers game.

How to Play against Agent

When asked the question ./s_ab_3/first_weights File exists: use weights:(y)/n:, press enter

See game play between Alpha-Beta and SARSA agent

To see Alpha Beta agent and Reinforcement learning agent playing games, enter following command:

python checkers.py -f sl -s ab -z ./s_ab_3/first_weights -l 0

Play against SARSA agent

To play game against SARSA agent, enter following command:

python checkers.py -f sl -s k -z ./s_ab_3/first_weights -l 0

Press enter when you see a blinking cursor and no input is asked. To enter moves when asked, for example to move a piece from position [x1, y1] to [x2, y2], in start position enter x1 y1 press enter in end position enter x2 y2 press enter

When there are multiple attack moves like [x1, y1] to [x2, y2] to [x3, y3], in start position enter x1 y1 press enter in end position enter x2 y2 x3 y3 press enter

Play against Alpha-Beta agent

To play game against alphabeta agent, enter following command:

python checkers.py -f ab -s k

About Alpha-Beta agent

This agent is Minimax agent with Alpha-Beta pruning.

To create this agent, search upto depth 3 is performed, and then evaluation function is used.

Evaluation function is as following:

1. If minimax agent wins, +500
2. If minimax agent loses, −500
3. If none of the above happens, evaluation function is summation of following values:
   • (1) times the number of minimax agent’s pawns
   • (2) times the number of minimax agent’s kings
   • (−1) times the number of opponent’s pawns
   • (−2) times the number of opponent’s kings

About SARSA agent

Assume that agent is in state s and has many choices for action a, and for each choice of action, environment takes agent to a state s'. For our reinforcement learning agent, we used following features to represent state action (s, a) pairs:

1. Number of agent’s pawns in s
2. Number of agent’s kings in s
3. Number of opponent’s pawns in s
4. Number of opponent’s kings in s
5. Difference between number of opponent’s pawns in state s' and s
6. Difference between number of opponent’s kings in state s' and s
7. Difference between total number of opponent’s pieces in state s' and s
8. Number of agent’s pieces being attacked by opponent in state s'

Reward function

The agent takes action a in state s and moves to state s'. The opponent takes action in state s' and moves to state s''. The environment will then given the reward to the agent for the action a in state s. Thus reward function depends on current state of the agent s, the action it took a and the state in which it will take next action s''.

• Reward function is sum of the following:
  • (−0:4) x (number of agent’s pawns in s − number of agent’s pawns in s'')
  • (−0:5) x (number of agent’s kings in s − number of agent’s kings in s'')
  • (+0:2) x (number of opponent’s pawns in s − number of opponent’s pawns in s'')
  • (+0:3) x (number of opponent’s kings in s − number of opponent’s kings in s'')
• If agent wins in state s'' or s', reward of (+500)
• If agent loses in state s'' or s', reward of (−500)
• If all of above is 0, a living reward of (−0.1)

Acknowledgements

Board class for checkers game specified in game.py is adapted from the project of SamRagusa.