gayathrig269 / CMPE260_Rfmt_Learning_TicTacToe

This project is about making a computer play TicTacToe games using Reinforcement Learning concepts

CMPE260_Rfmt_Learning_TicTacToe

To train or teach a computer on how to play the game of Tic-Tac-Toe using different Reinforcement Learning concepts like Min Max algorithm to Neural Networks.

Agent – Player of the game, either Min-Max agent, Tabular Q agent, or Neural Network agent. State – State of the game when a player makes move. Action – Marking a Cross or a Naught on the game board. Environment – The space where agent moves, it will be a 3X3 Tic-Tac-Toe board. Policy – The algorithm used, either Tabular Q Learning or Min-Max in deterministic fashion. Reward/Punishment – A player wins or loses the game.

Part 0: Setting up a 3X3 Tic-Tac-Toe board and introduces a Random player

This notebook contains 3X3 board initialization, initiates a Random player.

Part 1: Min-Max

Using Minimum-Maximum algorithm, we train the agent to play against Random Player and itself.

Min-Max Strategy:

1. Assumptions Make the best move (max. game value for us) Opp. will also make the best move.(min. the game value for us)
2. Internal cache storage – game board position. Compute all possible continuations from a given position only once. Results are plotted and statistics is taken for conclusion.

Part 2: Tabular QLearning

Using Tabular Q Learning policy, training the agent to perform better than Min-Max agents and Random player.Results are plotted and statistics is taken for conclusion.

Tabular Q Function : By looking up the Q values for all possible moves in the current situation and chose the one with the highest value. Rewards Best move -1 Loss - 0 Draw – 0.5 Learning rate(alpha) – 0.9 and (Discount factor)gamma – 0.95

Part 3: Neural Network Q Learning

QLearning strategy used, a # Neural Network is built to play against other agents.

We implement the Neural Network Q Learning player in the following way:

  1. An input layer which takes a game state, i.e., the current board, as input.   2. One or more hidden layer.   3. An output layer which will output the Q value for all possible moves in that game state.   4. We use Mean Squared Error as our loss function, which is a generic and popular loss function for regression, i.e., learning to mimic another function.   5. The input for the loss function will be the output of the Neural Network and our updated estimate of the Q function by applying the discounted rewards and maximum Q  values of the next states. i.e., the loss will be the difference between the output of the Neural Network and our updated estimate of the Q function.   6. We use the Gradient Descent Optimizer for training — i.e., to adjust the weights in the Neural Network.    7. We use ReLu activation function.

We used other activation functions like tanh, sigmoid, leaky reLU and other optimzer- Adam.

Experimented with Epsilon parameter to overcome the more greedy approach. Plots against other RL agent are analysed for every battle.

Conclusion:

1. Tabular Q Player learns a strategy after a lot of games. Not feasable for games like Go,Chess etc. which require a lot of states S.
2. Min-Max Player does not require any learning, but does assume that opponent plays best.
3. Neural network player uses greedy strategy and avoids risks, exploration. Only the last game is fed into the network, so network assumes receives negative feedback, if the game was was lost. Epsilon greedy strategy saw the agent perform better.