https://github.com/chen0040/java-reinforcement-learning-tic-tac-toe
Demo of reinforcement learning using tic-tac-toe
https://github.com/chen0040/java-reinforcement-learning-tic-tac-toe
q-learning reinforcement-learning sarsa tic-tac-toe
Last synced: 3 months ago
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Demo of reinforcement learning using tic-tac-toe
- Host: GitHub
- URL: https://github.com/chen0040/java-reinforcement-learning-tic-tac-toe
- Owner: chen0040
- License: mit
- Created: 2017-11-29T01:42:53.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-18T02:44:18.000Z (almost 8 years ago)
- Last Synced: 2025-06-01T20:02:05.123Z (4 months ago)
- Topics: q-learning, reinforcement-learning, sarsa, tic-tac-toe
- Language: Java
- Size: 31.3 KB
- Stars: 8
- Watchers: 1
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# java-reinforcement-learning-tic-tac-toe
Demo of reinforcement learning using tic-tac-toe with the [java-reinforcement-learning](https://github.com/chen0040/java-reinforcement-learning) package.
# Usage - GUI
The gui version run reinforcement learning in the interactive mode. it is implemented in com.github.chen0040.jrl.ttt.gui.Application
To run the gui version, git clone this project, run the make.ps1 or make.sh to build the reinforcement-learning-tic-tac-toe.jar into the bin folder.
Run:
```bash
java -jar bin/reinforcement-learning-tic-tac-toe.jar
```
# Usage - Console
The console version runs the reinforcement learning against a naive bot, they are implemented in:
* com.github.chen0040.jrl.ttt.dojos.DojoQ
* com.github.chen0040.jrl.ttt.dojos.DojoSarsa
* com.github.chen0040.jrl.ttt.dojos.DojoR
### Q-Learn
The following create two Q-Learning bots that plays against each other on the tic-tac-toe game to a train a Q-Learn model:
```java
public static QLearner train(Board board, int episodes) {
int stateCount = (int)Math.pow(3, board.size() * board.size());
int actionCount = board.size() * board.size();
QLearner learner = new QLearner(stateCount, actionCount);
//learner.setActionSelection(SoftMaxActionSelectionStrategy.class.getCanonicalName());
QBot bot1 = new QBot(1, board, learner);
QBot bot2 =new QBot(2, board, learner);
int wins = 0;
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
logger.info("winner: {}", board.getWinner());
bot1.updateStrategy();
bot2.updateStrategy();
logger.info("board: \n{}", board);
wins += board.getWinner() == 1 ? 1 : 0;
logger.info("success rate: {} %", (wins * 100) / (i+1));
}
return learner;
}
```
Alternaitvely, the following create one QBot that plays against a NaiveBot on the tic-tac-toe game to a train a Q-Learn model:
```java
public static QLearner train(Board board, int episodes) {
int stateCount = (int)Math.pow(3, board.size() * board.size());
int actionCount = board.size() * board.size();
QLearner learner = new QLearner(stateCount, actionCount);
//learner.setActionSelection(SoftMaxActionSelectionStrategy.class.getCanonicalName());
QBot bot1 = new SarsaBot(1, board, learner);
NaiveBot bot2 =new SarsaBot(2, board);
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
logger.info("winner: {}", board.getWinner());
bot1.updateStrategy();
logger.info("board: \n{}", board);
}
return learner;
}
```
The following code uses the trained Q-Learn model to create a QBot (bot1) which plays against bot2 (which is a naive bot that randomly takes a possible action each step):
```java
public static double test(Board board, QLearner model, int episodes) {
QBot bot1 = new QBot(1, board, model);
NaiveBot bot2 =new NaiveBot(2, board);
int wins = 0;
int loses = 0;
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
int winner = board.getWinner();
logger.info("Winner: {}", winner);
wins += winner == 1 ? 1 : 0;
loses += winner == 2 ? 1 : 0;
}
return wins * 1.0 / episodes;
}
```
The following code shows how to train and then test a Q-Learn model:
```java
Board board = new Board();
QLearner model = train(board, 30000);
double successRate = test(board, model, 1000);
logger.info("Q-Learn Bot beats Random Bot in {} % of the games being played", successRate * 100);
```
To save and load the Q-Learn model:
```java
String model_json = model.toJson();
QLearner loaded_from_json = QLearner.fromJson(model_json);
```
This sample code can be found in the DojoQ.java file in the project.
### SARSA (State-Action-Reward-State-Action)
The following create two SARSA bots that plays against each other on the tic-tac-toe game to a train a SARSA model:
```java
public static QLearner train(Board board, int episodes) {
int stateCount = (int)Math.pow(3, board.size() * board.size());
int actionCount = board.size() * board.size();
SarsaLearner learner = new SarsaLearner(stateCount, actionCount);
//learner.setActionSelection(SoftMaxActionSelectionStrategy.class.getCanonicalName());
SarsaBot bot1 = new SarsaBot(1, board, learner);
SarsaBot bot2 =new SarsaBot(2, board, learner);
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
logger.info("winner: {}", board.getWinner());
bot1.updateStrategy();
bot2.updateStrategy();
logger.info("board: \n{}", board);
}
return learner;
}
```
Alternaitvely, the following create one SARSA bot that plays against a NaiveBot on the tic-tac-toe game to a train a SARSA model:
```java
public static QLearner train(Board board, int episodes) {
int stateCount = (int)Math.pow(3, board.size() * board.size());
int actionCount = board.size() * board.size();
SarsaLearner learner = new SarsaLearner(stateCount, actionCount);
//learner.setActionSelection(SoftMaxActionSelectionStrategy.class.getCanonicalName());
SarsaBot bot1 = new SarsaBot(1, board, learner);
NaiveBot bot2 =new SarsaBot(2, board);
int wins = 0;
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
logger.info("winner: {}", board.getWinner());
bot1.updateStrategy();
logger.info("board: \n{}", board);
wins += board.getWinner() == 1 ? 1 : 0;
logger.info("success rate: {} %", (wins * 100) / (i+1));
}
return learner;
}
```
The following code uses the trained SARSA model to create a SarsaBot (bot1) which plays against bot2 (which is a naive bot that randomly takes a possible action each step):
```java
public static double test(Board board, SarsaLearner model, int episodes) {
SarsaBot bot1 = new SarsaBot(1, board, model);
NaiveBot bot2 =new NaiveBot(2, board);
int wins = 0;
int loses = 0;
for(int i=0; i < episodes; ++i) {
bot1.clearHistory();
bot2.clearHistory();
logger.info("Iteration: {} / {}", (i+1), episodes);
board.reset();
while (board.canBePlayed()) {
bot1.act();
bot2.act();
}
int winner = board.getWinner();
logger.info("Winner: {}", winner);
wins += winner == 1 ? 1 : 0;
loses += winner == 2 ? 1 : 0;
}
return wins * 1.0 / episodes;
}
```
The following code shows how to train and then test a SARSA model:
```java
Board board = new Board();
SarsaLearner model = train(board, 30000);
double successRate = test(board, model, 1000);
logger.info("SARSA Bot beats Random Bot in {} % of the games being played", successRate * 100);
```
To save and load the SARSA model:
```java
String model_json = model.toJson();
SarsaLearner loaded_from_json = SarsaLearner.fromJson(model_json);
```
This sample code can be found in the DojoSarsa.java file in the project.