reinforcement-learning
v1.0.20
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Reinforcement learning in javascript
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reinforcement-learning
Easy reinforcement learning using tensorflow.js
[X] Deep Q Network (DQN)
[ ] Genetic Algorithim (GA)
[ ] Examples
[ ] Training Dashboard (Tensorboard)
Require
const rl = require('reinforcement-learning');
DQN
Parameters
arch
- Architechture of the neural networkepsilon
- % of actions that should be taken randomly for explorationepsilonDecay
- Epsilon will be multiplied by this amount every episodereplayMemorySize
- Amount of previous steps left in memory to train onminiBatchSize
- Batch size to fit onactionSpaceSize
- Amount of possible actions the agent can takeminReplaySize
- Minimum amount of memories allowed for fittingupdateTargetEvery
How many episodes to wait to update the predictions networkaccuracyLookbackSize
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});
})();