java-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