@brain/rl

@brain/rl is a Reinforcement Learning library that implements several common RL algorithms, all with web demos. In particular, the library currently includes:

NOTE: A typescript fork of the reinforce.js library from https://github.com/karpathy/reinforcejs. An effort to keep the library current and usable in the modern world. Thank you Andrej karpathy (original author) for your fine work.

Examples

NOTE: Below is outdated.

• Dynamic Programming methods
• (Tabular) Temporal Difference Learning (SARSA/Q-Learning)
• Deep Q-Learning for Q-Learning with function approximation with Neural Networks
• Stochastic/Deterministic Policy Gradients and Actor Critic architectures for dealing with continuous action spaces. (very alpha, likely buggy or at the very least finicky and inconsistent)

See the main webpage for many more details, documentation and demos.

Code Sketch

The library exports two global variables: R, and RL. The former contains various kinds of utilities for building expression graphs (e.g. LSTMs) and performing automatic backpropagation, and is a fork of my other project recurrentjs. The RL object contains the current implementations:

• RL.DPAgent for finite state/action spaces with environment dynamics
• RL.TDAgent for finite state/action spaces
• RL.DQNAgent for continuous state features but discrete actions

A typical usage might look something like:

// create an environment object
var env = {};
env.getNumStates = function() { return 8; }
env.getMaxNumActions = function() { return 4; }

// create the DQN agent
var spec = { alpha: 0.01 } // see full options on DQN page
agent = new RL.DQNAgent(env, spec); 

setInterval(function(){ // start the learning loop
  var action = agent.act(s); // s is an array of length 8
  //... execute action in environment and get the reward
  agent.learn(reward); // the agent improves its Q,policy,model, etc. reward is a float
}, 0);

The full documentation and demos are on the main webpage.

License

MIT.