Adaptive Decision-Making Technique in Open-Ended Environments

open_world_agent_logic.js

const n = 10; // Number of possible actions
const z = 20; // Size of the window
const s = 5;  // Size of the action vector
const k = 3;  // Size of the position vector

// Generates a random action vector
function generateRandomActions() {
    let actions = [];
    for (let i = 0; i < s; i++) {
        actions.push(Math.floor(Math.random() * n));
    }
    return actions;
}

// Generates a random positions vector
function generateRandomPositions() {
    let positions = [];
    for (let i = 0; i < k; i++) {
        positions.push(Math.floor(Math.random() * z));
    }
    return positions;
}

// Simulates retrieving data from the window 'z'
function getDataFromWindow(positions) {
    return positions.map(p => Math.random()); // Returns random data
}

// Calculates the cumulative modular sum
function cumulativeModularSum(data) {
    return data.reduce((acc, val) => (acc + val) % s, 0);
}

// Evaluates the action (this function is a placeholder)
function evaluateAction(action) {
    // A simple evaluation function could be something related to the action itself
    return action; 
}

let bestScore = -Infinity;
let bestAction = null;

for (let i = 0; i < 100; i++) { // Runs for 100 iterations
    let actions = generateRandomActions();
    let positions = generateRandomPositions();
    let windowData = getDataFromWindow(positions);
    let actionIndex = cumulativeModularSum(windowData);
    let action = actions[actionIndex];
    let score = evaluateAction(action);

    if (score > bestScore) {
        bestScore = score;
        bestAction = action;
        // Implement the 1% adjustment logic here if necessary
    }
    // Implement the logic for reset and 1% randomization if the score is worse
}

console.log(`Best action: ${bestAction} with score: ${bestScore}`);