为什么我的 AI 模型可以训练但不会进化 - ML Agents

Question

创建了一个简单的统一游戏，球应该击中目标而不击中墙壁。于是，开始训练，结果太糟糕了。球只是收集 4 个目标之一。但是 EndEpisode() 在收集到最后一个目标时发生。

Screenshot of the scene and the balls path throughout the training of 1650,000 steps(if im not wrong, since I called it a generation for every 10,000 steps of training.)

球甚至没有尝试击中第二个目标。我的代码有什么问题？

我什至尝试过用 RayPerceptionSensor3D 将球体替换为圆柱体，这样它就不会翻转并干扰 rayperceptionSensor3d。但它给出了更糟糕的结果。

using System.Security.Cryptography;
using System.Data.SqlTypes;
using System.Security;
using System.Runtime.InteropServices;
using System.Net.Sockets;
using System.ComponentModel.Design.Serialization;
using System.Collections.Generic;
using UnityEngine;
using MLAgents;
using MLAgents.Sensors;
using TMPro;

public class MazeRoller : Agent
{

    Rigidbody rBody;
    Vector3 ballpos;
    void Start () {
        rBody = GetComponent<Rigidbody>();
        ballpos = rBody.transform.position;
    }


    public TextMeshPro text;
    public TextMeshPro miss;
    public TextMeshPro hit;
    int count=0,c=0,h=0,m=0;

    int boxescollect=0;

    public Transform Target;
    public Transform st1;
    public Transform st2;
    public Transform st3;

    public override void OnEpisodeBegin()
    {
        rBody.angularVelocity = Vector3.zero;
        rBody.velocity = Vector3.zero;
        rBody.transform.position = ballpos;
        boxescollect=0;

        st1.GetComponent<Renderer> ().enabled = true;
        st1.GetComponent<Collider> ().enabled = true;

        st2.GetComponent<Renderer> ().enabled = true;
        st2.GetComponent<Collider> ().enabled = true;

        st3.GetComponent<Renderer> ().enabled = true;
        st3.GetComponent<Collider> ().enabled = true;
    }


    void OnCollisionEnter(Collision collision)
    {
        if(collision.gameObject.name == "Target")
        {
            if(st1.GetComponent<Renderer> ().enabled==true || st2.GetComponent<Renderer> ().enabled==true || st3.GetComponent<Renderer> ().enabled==true)
            {
                SetReward(-3.0f+(float)(boxescollect));
            }

            SetReward(2.0f);

            h++;
            hit.SetText(h+"");

            EndEpisode();
        }

        else if(collision.gameObject.name == "Target1")
        {
            boxescollect++;
            AddReward(0.2f);
            st1.GetComponent<Renderer> ().enabled = false;
            st1.GetComponent<Collider> ().enabled = false;
        }

        else if(collision.gameObject.name == "Target2")
        {
            boxescollect++;
            AddReward(0.4f);
            st2.GetComponent<Renderer> ().enabled = false;
            st2.GetComponent<Collider> ().enabled = false;
        }

        else if(collision.gameObject.name == "Target3")
        {
            boxescollect++;
            AddReward(0.6f);
            st3.GetComponent<Renderer> ().enabled = false;
            st3.GetComponent<Collider> ().enabled = false;

        }

        //collision.gameObject.name == "wall1"||collision.gameObject.name == "wall2"||collision.gameObject.name == "wall3"||collision.gameObject.name == "wall4"||collision.gameObject.name == "wall5"||collision.gameObject.name == "wall6"||collision.gameObject.name == "wall7"

        else if(collision.gameObject.tag == "wall")
        {

            if(st1.GetComponent<Renderer> ().enabled==true || st2.GetComponent<Renderer> ().enabled==true || st3.GetComponent<Renderer> ().enabled==true)
            {
                AddReward(-3.0f+(float)(boxescollect));
            }

            SetReward(-1.0f);
            m++;
            miss.SetText(m+"");
            EndEpisode();
        }


    }

    public override void CollectObservations(VectorSensor sensor)
    {
        // Target and Agent positions
        sensor.AddObservation(Target.position);
        sensor.AddObservation(this.transform.position);

        sensor.AddObservation(boxescollect);
        sensor.AddObservation(boxescollect-3);

        sensor.AddObservation(st1.position);
        sensor.AddObservation(st2.position);
        sensor.AddObservation(st3.position);


        float dist = Vector3.Distance(Target.position,this.transform.position);
        //Distance between Agent and target
        sensor.AddObservation(dist);

        float d1 = Vector3.Distance(st1.position,this.transform.position);
        //Distance between Agent and target
        sensor.AddObservation(d1);


        float d2 = Vector3.Distance(st2.position,this.transform.position);
        //Distance between Agent and target
        sensor.AddObservation(d2);


        float d3 = Vector3.Distance(st3.position,this.transform.position);
        //Distance between Agent and target
        sensor.AddObservation(d3);

        // Agent velocity
        sensor.AddObservation(rBody.velocity.x);
        sensor.AddObservation(rBody.velocity.z);
    }

    public float speed = 10;
    public override void OnActionReceived(float[] vectorAction)
    {
        Vector3 controlSignal = Vector3.zero;
        controlSignal.x = vectorAction[0];
        controlSignal.z = vectorAction[1];
        //speed = vectorAction[2];
        rBody.AddForce(controlSignal * speed);
        //speed=0;

        count++;

        if(count==10000)
        {

            count=0;
            h=0;
            m=0;
            c++;
            miss.SetText(m+"");
            hit.SetText(h+"");
            text.SetText(c+"");
        }

    }

    public override float[] Heuristic()
    {
        var action = new float[2];
        action[0] = Input.GetAxis("Horizontal");
        action[1] = Input.GetAxis("Vertical");
        return action;
    }
}

weird Graph of the training - tensorboard 这是我在tensorboard训练后得到的。

Answer 1

你在结束这一集时只完成了一个目标，而不是为了完全实现你的目标。所以你的图表看起来很乱，它结束得太早了，特工不明白它的目的。

我认为您可以添加一些新规则。 -如果代理人退缩了，他就会受到惩罚 -如果在情节结束前没有拿走所有4个立方体，代理人将受到惩罚

只有当智能体完成拿走所有 4 个立方体的任务（奖励）或者如果智能体采取了一些步骤而没有实现他的目标（惩罚）时，情节才应该结束

希望对您有所帮助。我觉得我的英语不好。

___edit 2:___

您的问题很可能与本文档中描述的问题具有相似的特征。（特别是第 28 页）

https://repositorio.upct.es/bitstream/handle/10317/8094/tfg-san-est.pdf?sequence=1&isAllowed=y （对不起，这是西班牙语，但是 google 翻译器会给你一个相当准确的翻译。）

文档中的问题与您的问题相同，代理在拐角处出现问题，当他到达拐角时他returns到达起点，仅在拐角处出现这种情况。

你试过换景吗？也许......尝试不带墙，看看代理是否真的在寻找 "all" 目标并更深入地研究问题。

图表是其中最少的，它只是一个表示。如果代理没有完成他的任务，你将不会有一个好的图表。

为什么我的 AI 模型可以训练但不会进化 - ML Agents

Why is my AI model trains but doesn't evolve - ML Agents

c#

unity3d

tensorboard

reinforcement-learning

ml-agent