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@brain/rl
Advanced tools
**@brain/rl** is a Reinforcement Learning library that implements several common RL algorithms, all with web demos. In particular, the library currently includes:
Readme
@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.
NOTE: Below is outdated.
See the main webpage for many more details, documentation and demos.
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 dynamicsRL.TDAgent
for finite state/action spacesRL.DQNAgent
for continuous state features but discrete actionsA 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.
MIT.
FAQs
**@brain/rl** is a Reinforcement Learning library that implements several common RL algorithms, all with web demos. In particular, the library currently includes:
The npm package @brain/rl receives a total of 2 weekly downloads. As such, @brain/rl popularity was classified as not popular.
We found that @brain/rl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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