In this post, we'll learn how to build reinforcement learning agents in TensorFlow.js and Node.js. We'll start by briefly covering the basics of reinforcement learning. We'll then see how to install TensorFlow.js and its dependencies. Finally, we'll write a simple Node.js script that trains a reinforcement learning agent to play a simple game.
Reinforcement learning is a type of machine learning that enables agents to learn by taking actions in an environment and receiving rewards for their actions. The goal of reinforcement learning is to maximize the total reward that the agent receives.
There are two main types of reinforcement learning:
In this post, we'll focus on discrete reinforcement learning.
Before we can start building reinforcement learning agents, we need to install TensorFlow.js and its dependencies.
First, we need to install Node.js. Node.js is a JavaScript runtime that enables us to run JavaScript code outside of a web browser.
Second, we need to install the TensorFlow.js Node.js API. The TensorFlow.js Node.js API enables us to use TensorFlow.js in a Node.js environment.
Third, we need to install the reinforcement learning library for TensorFlow.js. The reinforcement learning library for TensorFlow.js enables us to build reinforcement learning agents in TensorFlow.js.
Fourth, we need to install a game engine. In this post, we'll use the Simple Game Engine. The Simple Game Engine is a lightweight game engine that makes it easy to develop and test reinforcement learning agents.
Finally, we need to install a text editor. In this post, we'll use Visual Studio Code. Visual Studio Code is a free and open-source text editor from Microsoft.
Now that we have TensorFlow.js and its dependencies installed, we can write a Node.js script that trains a reinforcement learning agent.
First, we need to require the dependencies that we installed. We require the TensorFlow.js Node.js API, the reinforcement learning library for TensorFlow.js, and the Simple Game Engine.
const tf = require('@tensorflow/tfjs-node');
const rl = require('@tensorflow-models/rl');
const { SimpleGameEngine } = require('simple-game-engine');
Next, we need to define the game that the agent will play. We do this by creating a new instance of the SimpleGameEngine class.
const game = new SimpleGameEngine();
The SimpleGameEngine class has a number of methods that we can use to define the game. In this example, we'll use the addEntity() method to add a player to the game.
game.addEntity({
id: 'player',
x: 0,
y: 0,
width: 10,
height: 10
});
We can also use the addEntity() method to add obstacles to the game.
game.addEntity({
id: 'obstacle1',
x: 50,
y: 50,
width: 10,
height: 10
});
game.addEntity({
id: 'obstacle2',
x: 100,
y: 100,
width: 10,
height: 10
});
Now that we have defined the game, we need to define the rules of the game. In this example, the goal of the game is to avoid the obstacles and reach the end of the level. The level is 50 units wide and the player starts at the left side of the level. The player can move left, right, up, or down. If the player collides with an obstacle, the game ends. If the player reaches the right side of the level, the game ends and the player receives a reward of 100.
game.setRules({
win: ['atXPos', 'player', 50],
lose: ['collides', 'player', ['obstacle1', 'obstacle2']],
endOnWin: true
});
We can also use the setRules() method to define other rules for the game. For example, we could use the setRules() method to define a time limit for the game.
Now that we have defined the game, we need to define the environment. The environment is the interface between the agent and the game. In this example, we'll use the SimpleGameEngineEnvironment class from the reinforcement learning library for TensorFlow.js.
const environment = new rl.SimpleGameEngineEnvironment(game);
The SimpleGameEngineEnvironment class has a number of methods that we can use to interact with the environment. In this example, we'll use the reset() method to reset the environment and the step() method to take a step in the environment.
environment.reset();
while (!environment.isDone()) {
const action = environment.actionSpace.sample();
environment.step(action);
}
The actionSpace property is a space that contains all of the possible actions that the agent can take. In this example, we use the sample() method to randomly sample an action from the action space.
Now that we have defined the environment, we need to define the agent. The agent is the reinforcement learning algorithm that we'll use to train the agent. In this example, we'll use the DQN() method from the reinforcement learning library for TensorFlow.js.
const agent = new rl.DQN({
environment,
numEpisodes: 100,
trainInterval: 10
});
The DQN() method takes a number of options as arguments. In this example, we use the numEpisodes option to specify the number of episodes that we want the agent to train for. We also use the trainInterval option to specify the interval at which we want the agent to train.
Now that we have defined the agent, we need to train the agent. We do this by calling the train() method on the agent.
agent.train();
Once the agent has been trained, we can use the save() method to save the trained agent.
agent.save('path/to/agent');
We can also use the load() method to load a trained agent.
agent.load('path/to/agent');
In this post, we've learned how to build reinforcement learning agents in TensorFlow.js and Node.js. We've seen how to install TensorFlow.js and its dependencies. We've also written a simple Node.js script that trains a reinforcement learning agent to play a simple game.