script.js

// 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
}