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ReImproveJS
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| import assert from 'assert' | |
| import { NeuralNetwork, Model, Academy } from "reimprovejs/dist/reimprove.js" | |
| const TIMEOUT = 1; // mins | |
| const MAP_SIZE = 10; | |
| function randomPoint() | |
| { | |
| let min = 0; | |
| let max = MAP_SIZE; | |
| return Math.floor(Math.random()*(max-min 1) min); | |
| } | |
| function jumpDistance(x1, y1, x2, y2) { | |
| return Math.abs(x2-x1) Math.abs(y2-y1); | |
| } | |
| // const average = arr => arr.reduce( ( p, c ) => p c, 0 ) / arr.length; | |
| suite('Academy tests', function () { | |
| this.timeout(TIMEOUT * 60 * 1000); | |
| test('setup academy and train students', async function () { | |
| let actor = {x: 1, y: 1}; | |
| let target = {x: 5, y: 7}; | |
| let distance = jumpDistance(actor.x, actor.y, target.x, target.y); | |
| let steps = 0; | |
| const modelFitConfig = { | |
| epochs: 1, | |
| stepsPerEpoch: 16 | |
| }; | |
| const numActions = 4; | |
| const inputSize = 4; | |
| // The window of data which will be sent yo your agent. For instance the x previous inputs, and what actions the agent took | |
| const temporalWindow = 1; | |
| const totalInputSize = inputSize * temporalWindow numActions * temporalWindow inputSize; | |
| const network = new NeuralNetwork(); | |
| network.InputShape = [totalInputSize]; | |
| network.addNeuralNetworkLayers([ | |
| {type: 'dense', units: 32, activation: 'relu'}, | |
| {type: 'dense', units: numActions, activation: 'softmax'} | |
| ]); | |
| // Now we initialize our model, and start adding layers | |
| const model = new Model.FromNetwork(network, modelFitConfig); | |
| // Finally compile the model, we also exactly use tfjs's optimizers and loss functions | |
| // (So feel free to choose one among tfjs's) | |
| model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}) | |
| // Every single field here is optionnal, and has a default value. Be careful, it may not fit your needs ... | |
| const teacherConfig = { | |
| lessonsQuantity: 10000, | |
| lessonLength: 20, | |
| lessonsWithRandom: 2, | |
| epsilon: 0.5, | |
| epsilonDecay: 0.995, | |
| epsilonMin: 0.05, | |
| gamma: 0.9 | |
| }; | |
| const agentConfig = { | |
| model: model, | |
| agentConfig: { | |
| memorySize: 1000, // The size of the agent's memory (Q-Learning) | |
| batchSize: 128, // How many tensors will be given to the network when fit | |
| temporalWindow: temporalWindow // The temporal window giving previous inputs & actions | |
| } | |
| }; | |
| // First we need an academy to host everything | |
| const academy = new Academy(); | |
| const teacher = academy.addTeacher(teacherConfig); | |
| const agent = academy.addAgent(agentConfig); | |
| academy.assignTeacherToAgent(agent, teacher); | |
| while(true) { | |
| // Gather inputs | |
| let distance_before = Math.hypot(target.x-actor.x, target.y-actor.y); | |
| let inputs = [actor.x, actor.y, target.x, target.y]; | |
| assert.equal(inputs.length, inputSize, "The Input Size dose not match the Inputs Array length"); | |
| // Step the learning | |
| let result = await academy.step([{teacherName: teacher, agentsInput: inputs}]); | |
| // Take Action | |
| if(result !== undefined) { | |
| steps ; | |
| var action = result.get(agent); | |
| if(action === 0) { | |
| actor.x ; // Right | |
| } else if(action === 1) { | |
| actor.x--; // Left | |
| } else if(action === 2) { | |
| actor.y ; // Down | |
| } else if(action === 3) { | |
| actor.y--; // Up | |
| } | |
| } | |
| if(actor.x < 0) | |
| actor.x = 0; | |
| else if(actor.x > MAP_SIZE) | |
| actor.x = MAP_SIZE; | |
| if(actor.y < 0) | |
| actor.y = 0; | |
| else if(actor.y > MAP_SIZE) | |
| actor.y = MAP_SIZE; | |
| let distance_after = Math.hypot(target.x-actor.x, target.y-actor.y) | |
| let reward = (distance_before == distance_after) ? -0.1 : distance_before - distance_after; | |
| academy.addRewardToAgent(agent, reward); | |
| // console.info(`Target: (${target.x}, ${target.y}) Location: (${actor.x}, ${actor.y}) Reward: ${reward}`); | |
| if(actor.x === target.x && actor.y === target.y) { | |
| console.info(`Target: ${distance} Steps: ${steps} Delta: ${(steps-distance)}`); | |
| target = { x: randomPoint(), y: randomPoint() }; | |
| steps = 0; | |
| distance = jumpDistance(actor.x, actor.y, target.x, target.y); | |
| } | |
| } | |
| }); | |
| teardown(async function () { | |
| // Do Nothing... | |
| }); | |
| }); |
@T2brozz make sure that the function that contains that line is declared as an async function.
const func = () => { await asyncFunc(); };
will always throw the error you mentioned. To declare it an async function, you need to do the following (this syntax is for arrow functions):
const func = async () => { await asyncFun(); };
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hey you use
await academy.step([{teacherName: teacher, agentsInput: inputs}]);at this line on mycode (not copyed ) I getSyntaxError: await is only valid in async functions and async generators. I use your line in a setInterval function . Do you have any suggestions?