unity中关于MLAGENTS支持智能AI给稍微复杂的变化环境下的AI像人类玩家一样思考，带来了极大惊喜。分享一下机器学习的模式。
如图：
给定的剧情比较简单，要求AI尽可能在纷繁复杂的物体中间捡拾绿色物体，就跟AI需要靠近某些攻击对象或者分类捡拾有用道具的需求基础。上一篇已经给大家介绍了环境的搭建，本篇之间干货。
using System.Collections;using System.Collections.Generic;using UnityEngine;using MLAgents;using System;public class NPCAgent : Agent{public GameObject ground;public GameObject redcube;public GameObject greencube;RayPerception rayPer;Rigidbody rd;public override void InitializeAgent(){base.InitializeAgent();rayPer = GetComponent<RayPerception>();rd = GetComponent<Rigidbody>();}public override void CollectObservations(){float rayDistance = 12f;float[] rayAngles = {20f,60f,90f,120f,160f};string[] detectableObjects = {"Cube+", "Cube-","wall" };AddVectorObs(GetStepCount()/(float)agentParameters.maxStep);AddVectorObs(rayPer.Perceive(rayDistance,rayAngles,detectableObjects,0f,0f));}public override void AgentAction(float[] vectorAction, string textAction){// AddReward(-1f/agentParameters.maxStep);MoveAgent(vectorAction);}private void MoveAgent(float[] vectorAction){if (GameObject.FindGameObjectsWithTag("Cube+").Length <= 0){Done();return;}Vector3 dirToGo = Vector3.zero;Vector3 rotateDir = Vector3.zero;dirToGo = transform.forward * Mathf.Clamp(vectorAction[0], -1f, 1f);rotateDir = transform.up * Mathf.Clamp(vectorAction[1], -1f, 1f);transform.Rotate(rotateDir,Time.deltaTime*150f);rd.AddForce(dirToGo*1.5f,ForceMode.VelocityChange);}void OnCollisionEnter(Collision col){if (pareTag("Cube+")){SetReward(1f);col.gameObject.transform.position= new Vector3(UnityEngine.Random.Range(-40f, 40f), 0, UnityEngine.Random.Range(-40f, 40f));Debug.Log("ok successful!");}if (pareTag("Cube-")){SetReward(-1f);col.gameObject.transform.position = new Vector3(UnityEngine.Random.Range(-40f, 40f), 0, UnityEngine.Random.Range(-40f, 40f));Debug.Log("sorry!");}if (pareTag("wall")){SetReward(-0.1f);}}public override void AgentReset(){transform.position = new Vector3(UnityEngine.Random.Range(-40f, 40f), 0.45f, UnityEngine.Random.Range(-40f, 40f));transform.rotation = Quaternion.Euler(0f, UnityEngine.Random.Range(0f, 360f), 0f);// transform.rotation = Quaternion.Euler(0f, 0f, 0f);rd.velocity *= 0f;for (int i = 0; i < GameObject.FindGameObjectsWithTag("Cube+").Length ; i++){GameObject.FindGameObjectsWithTag("Cube+")[i].transform.position = new Vector3(UnityEngine.Random.Range(-40f, 40f), 0, UnityEngine.Random.Range(-40f, 40f));}for (int i = 0; i < GameObject.FindGameObjectsWithTag("Cube-").Length; i++){GameObject.FindGameObjectsWithTag("Cube-")[i].transform.position = new Vector3(UnityEngine.Random.Range(-40f, 40f), 0, UnityEngine.Random.Range(-40f, 40f));}}}
当见到绿色Cube时奖励1分，见到有毒红色Cube惩罚1分，当碰到墙壁减掉0.1分,这是AI的奖励训练依据。
观察参数要求观察视觉看到的物体与自身的距离，这会让AI的大脑中尽量选择近距离的物体做选择。当然我发现我最后的训练结果就是，并不是一致像一个程序机器人从身边扫描，会跟人一样，有时会选择另外的方向角度去做下步选择，当然大部分是近距离先处理。
public override List<float> Perceive(float rayDistance,float[] rayAngles, string[] detectableObjects,float startOffset, float endOffset){perceptionBuffer.Clear();// For each ray sublist stores categorical information on detected object// along with object distance.foreach (float angle in rayAngles){endPosition = transform.TransformDirection(PolarToCartesian(rayDistance, angle));endPosition.y = endOffset;if (Application.isEditor){Debug.DrawRay(transform.position + new Vector3(0f, startOffset, 0f),endPosition, Color.black, 0.01f, true);}float[] subList = new float[detectableObjects.Length + 2];if (Physics.SphereCast(transform.position +new Vector3(0f, startOffset, 0f), 0.5f,endPosition, out hit, rayDistance)){for (int i = 0; i < detectableObjects.Length; i++){if (hit.pareTag(detectableObjects[i])){subList[i] = 1;subList[detectableObjects.Length + 1] = hit.distance / rayDistance;break;}}}else{subList[detectableObjects.Length] = 1f;}perceptionBuffer.AddRange(subList);}return perceptionBuffer;}
