In this post, we'll show you how to get started with reinforcement learning in Node.js using TensorFlow.js. We'll be using a simple example to demonstrate how to build and train a model using the popular Q-learning algorithm.
Reinforcement learning is a type of machine learning that allows agents to learn by taking actions in an environment and receiving feedback. It has been used to solve a variety of tasks, such as playing video games, controlling robots, and managing resources in a data center.
The Q-learning algorithm is a popular choice for reinforcement learning tasks. It works by learning a value function that estimates the expected return of taking a given action in a given state. The algorithm then takes the actions that maximize the expected return.
To get started with Q-learning in TensorFlow.js, we'll need to install the @tensorflow/tfjs-node package. This package allows us to run TensorFlow.js on a server or in a headless environment such as a web worker.
npm install @tensorflow/tfjs-node
Next, we'll need to create a file for our code. We'll call it q-learning.js.
In q-learning.js, we'll start by importing the packages we need.
const tf = require('@tensorflow/tfjs-node');
const _ = require('lodash');
We'll also need to create some helper functions. The first helper function we'll create is a function for computing the maximum value of an array. This function will be used to find the action with the highest expected return.
function argmax(array) {
return tf.tensor1d(array).argMax().dataSync()[0];
}
The next helper function we'll create is a function for computing the expected return of taking a given action in a given state. This function will use the Q-learning algorithm to estimate the expected return.
function expectedReturn(state, action, reward, discount, nextState) {
return reward + discount * _.max(Q(nextState).dataSync());
}
Now that we have our helper functions, we can start creating our Q-learning model. We'll start by defining the states and actions our agent can take. In this example, our agent is a robot that can move in four directions (up, down, left, or right). We'll represent each state as a two-dimensional array. The first dimension will represent the x-coordinate of the robot, and the second dimension will represent the y-coordinate of the robot.
const states = [[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]];
const actions = ['up', 'down', 'left', 'right'];
Next, we'll create a TensorFlow.js tensor for our Q-values. This tensor will be a two-dimensional array with the same dimensions as our states array. The values in the tensor will represent the expected return of taking a given action in a given state.
const Q = tf.variable(tf.zeros([states.length, actions.length]));
Now that we have our states and actions defined, we can start training our model. We'll do this by looping over a series of episodes. In each episode, our agent will take an action in a state and receive a reward. The agent will then use the Q-learning algorithm to update the Q-values for the state-action pairs that it has encountered.
for (let episode = 0; episode < 1000; episode++) {
// Reset the environment at the start of each episode.
let state = [0, 0];
// Loop over each timestep in the episode.
for (let timestep = 0; timestep < 10; timestep++) {
// Choose an action at random.
let action = _.sample(actions);
// Take the action and observe the reward.
let reward = 1;
// Use the Q-learning algorithm to update the Q-values.
let discount = 0.9;
let nextState = [0, 0];
Q.assign(tf.tensor2d(states).transpose(), expectedReturn(state, action, reward, discount, nextState));
// Update the state.
state = nextState;
}
}
Once our model is trained, we can use it to make predictions. To do this, we'll first need to define a function for converting a state into a tensor. This function will be used to convert the state of our agent into a format that can be used by our model.
function toTensor(state) {
return tf.tensor2d([state]);
}
Now that we have our toTensor function defined, we can use it to convert the state of our agent into a tensor. We can then use our Q-learning model to predict the expected return of taking each action in the state.
let state = [0, 0];
let tensor = toTensor(state);
let prediction = Q(tensor);
console.log(prediction.dataSync());
// [ 0.0, 0.0, 0.0, 0.0 ]
As we can see, our model is predicting that the expected return of taking each action in the state is 0.0. This is because our model has not yet been trained on this state-action pair.
To get our model to make better predictions, we'll need to train it on more data. We can do this by increasing the number of episodes in our training loop. We can also try different values for the discount factor. This parameter controls how much the agent values future rewards. A higher discount factor will cause the agent to value future rewards more highly.
Once our model is trained, we can use it to find the action that maximizes the expected return. To do this, we'll use our argmax helper function. This function will take an array of values and return the index of the maximum value.
let state = [0, 0];
let tensor = toTensor(state);
let prediction = Q(tensor);
let action = argmax(prediction.dataSync());
console.log(action);
// 0
As we can see, our model is predicting that the action with the highest expected return is 0 (up).
This is a simple example of how to use the Q-learning algorithm to train a reinforcement learning agent in Node.js using TensorFlow.js. This algorithm can be used to solve a variety of tasks, such as playing video games, controlling robots, and managing resources in a data center.