JarCastingjava-ai-minefield-navigator
Package provides java implementation of a multi-agent learning simulator based on FALCON ANN and RL
Introduction
TD-FALCON is a fusion architecture that incorporates temporal difference methods and self-organizing neural networks for reinforcement learning with delayed rewards. It learns by creating cognitive codes across sensory input, actions, and rewards.
The implementation of TD-FALCON can be found in the minefield navigation simulator in this java project, alongside with its reactive version R-FALCON, and a backpropagation network based Q-learning system.
Install
Add the following dependency to your POM file:
<dependency>
<groupId>com.github.cschen1205</groupId>
<artifactId>java-ai-minefield-navigator</artifactId>
<version>1.0.2</version>
</dependency>
Usage: Invoke GUI-based Simulator
The sample code below shows how to invoke the Q-Learn TD-FALCON MineField Navigator GUI:
public static void main(String[] args) {
MineFieldSimulatorConfig config = new MineFieldSimulatorConfig();
config.setImmediateRewardProvided(true);
config.setNumAgents(1);
FalconConfig falconConfig = new FalconConfig();
falconConfig.numAction = FalconNavAgent.numAction;
falconConfig.numState = config.numState();
falconConfig.numReward = 2;
MineFieldSimulatorGUI gui = new MineFieldSimulatorGUI(config, falconConfig);
}
Usage: Invoke Command-line based Simulator
The sample code below shows show to run the TD-FALCON MineField Navigator simulation in console mode:
public static void main(String[] args) {
MineFieldSimulatorConfig config = new MineFieldSimulatorConfig();
config.setImmediateRewardProvided(false);
config.setNumRuns(1);
config.setMaxTrial(300);
FalconConfig falconConfig = new FalconConfig();
falconConfig.numAction = FalconNavAgent.numAction;
falconConfig.numState = config.numState();
falconConfig.numReward = 2;
falconConfig.isBounded = false;
MineFieldSimulator simulator = new MineFieldSimulatorQ(config, falconConfig);
simulator.runSims();
}
The above codes invoke the Q-Learn TD-FALCON simulator, to switch to other learning paradigms, change the
"MineFieldSimulatorQ" to one of the following:
- MineFieldSimulatorSARSA: state-action-reward-state-action
- MineFieldSimulatorQLambda: Q-Learning with lambda trails
- MineFieldSimulatorSARSALambda: SARSA with lambda trails
- MineFieldSimulatorR: R-Learning