Reinforcement-learning NPM

reinforcement-learning

Easy reinforcement learning using tensorflow.js

Deep Q Network (DQN)
Genetic Algorithim (GA)
Examples
Training Dashboard (Tensorboard)

Require

const  rl = require('reinforcement-learning');

DQN

Parameters

arch - Architechture of the neural network

epsilon - % of actions that should be taken randomly for exploration
epsilonDecay - Epsilon will be multiplied by this amount every episode
replayMemorySize - Amount of previous steps left in memory to train on
miniBatchSize - Batch size to fit on
actionSpaceSize - Amount of possible actions the agent can take
minReplaySize - Minimum amount of memories allowed for fitting
updateTargetEvery How many episodes to wait to update the predictions network
accuracyLookbackSize How many previous steps should be used to calculate accuracy

const  rl = require('reinforcement-learning');
let  step = 0;


let  arch = [
{inputShape:  1, units:  14, activation:  'relu'},
{units:  2, activation:  'softmax'}
];
  
function  getState(){return [0];}

function  calcReward(state, action){
// Every 100 steps end the episode
step++;
let  episodeDone = false;
if(step === 100){episodeDone = true; step = 0;}

// Two armed bandit. Agent has to learn to always pick 1
if(action === 1)return {reward:  1, newState:[0], done:  true, episodeDone};
else{ return {reward:  0, newState:[0], done:  true, episodeDone}}
}


(async()=>{
let  agent = rl.DQN({
arch,
epsilon:  0,
epsilonDecay:  0,
replayMemorySize:  1000,
miniBatchSize:  64,
actionSpaceSize:  2,
minReplaySize:  100,
updateTargetEvery:  1,
accuracyLookbackSize:  500
});

  
await  agent.learn({accuracy:  95, getState, calcReward});

  
})();