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// Define the walking AI model
class WalkingAI extends tf.layers.Layer {
constructor(num_actions) {
super({});
this.num_actions = num_actions;
}
call(inputs) {
return tf.tidy(() => {
const x = tf.layers.dense({units: 32, activation: 'relu'}).apply(inputs);
const y = tf.layers.dense({units: 32, activation: 'relu'}).apply(x);
return tf.layers.dense({units: this.num_actions}).apply(y);
});
}
}
// Define the reinforcement learning agent
class ReinforcementAgent {
constructor(num_actions) {
this.num_actions = num_actions;
this.model = tf.model({inputs: tf.input({shape: [4]}), outputs: new WalkingAI(num_actions).apply});
this.optimizer = tf.train.adam(0.001);
}
getAction(state) {
return tf.tidy(() => {
const actionProbs = this.model.predict(tf.tensor(state, [1, 4]));
return tf.argMax(actionProbs, 1).dataSync()[0];
});
}
train(states, actions, rewards) {
tf.tidy(() => {
const target = tf.tensor(actions, [actions.length, 1]);
const loss = tf.losses.softmaxCrossEntropy(target, this.model.predict(states));
const grad = this.optimizer.computeGradients(() => loss.mean());
this.optimizer.applyGradients(grad.grads);
});
}
}
// Set up event listeners for UI buttons
document.getElementById("startButton").addEventListener("click", startAI);
document.getElementById("stopButton").addEventListener("click", stopAI);
// Function to start the AI
function startAI() {
const env = new gym.make('YourEnvName'); // Replace 'YourEnvName' with your environment name
const numActions = env.actionSpace.n;
const agent = new ReinforcementAgent(numActions);
// Training loop
const numEpisodes = 100; // Adjust the number of episodes as needed
const maxSteps = 200; // Adjust the maximum number of steps per episode as needed
for (let episode = 0; episode < numEpisodes; episode++) {
let state = env.reset();
let episodeReward = 0;
for (let step = 0; step < maxSteps; step++) {
// Get action from the agent
const action = agent.getAction(state);
// Take the action in the environment
const [nextState, reward, done, _] = env.step(action);
// Update the episode reward
episodeReward += reward;
// Store the experience in the agent's memory
agent.train([state], [action], [reward]);
// Transition to the next state
state = nextState;
// Update the environment display
updateEnvironmentDisplay(); // Implement this function
if (done) {
break;
}
}
// Print the episode reward
console.log("Episode:", episode, "Reward:", episodeReward);
// Update the AI output display
updateOutputDisplay(); // Implement this function
}
}
// Function to stop the AI
function stopAI() {
// Code to stop and reset the AI
}
// Function to update the environment display
function updateEnvironmentDisplay() {
// Code to update the environment display based on AI actions
}
// Function to update the AI output display
function updateOutputDisplay() {
// Code to update the AI output display based on AI actions or rewards
}
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