Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

150 行
4.5 KiB

using System.Collections.Generic;
using UnityEngine;
using System.Linq;
using Unity.MLAgents;
using UnityScript.Lang;
public class GridArea : MonoBehaviour
{
public enum CellType
{
Empty = 0,
Goal = 1,
Pit = 2,
Agent = 3,
}
[HideInInspector]
public List<GameObject> actorObjs;
[HideInInspector]
public CellType[] players;
[HideInInspector]
public CellType[,] board;
public GameObject trueAgent;
Camera m_AgentCam;
public GameObject goalPref;
public GameObject pitPref;
Dictionary<CellType, GameObject> m_Objects;
GameObject m_Plane;
GameObject m_Sn;
GameObject m_Ss;
GameObject m_Se;
GameObject m_Sw;
Vector3 m_InitialPosition;
EnvironmentParameters m_ResetParams;
public void Start()
{
m_ResetParams = Academy.Instance.EnvironmentParameters;
m_Objects = new Dictionary<CellType, GameObject>
{
{ CellType.Goal, goalPref },
{ CellType.Pit, pitPref }
};
m_AgentCam = transform.Find("agentCam")?.GetComponent<Camera>();
actorObjs = new List<GameObject>();
var sceneTransform = transform.Find("scene");
m_Plane = sceneTransform.Find("Plane").gameObject;
m_Sn = sceneTransform.Find("sN").gameObject;
m_Ss = sceneTransform.Find("sS").gameObject;
m_Sw = sceneTransform.Find("sW").gameObject;
m_Se = sceneTransform.Find("sE").gameObject;
m_InitialPosition = transform.position;
}
void SetEnvironment()
{
var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
transform.position = m_InitialPosition * (gridSize + 1);
var playersList = new List<CellType>();
for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numObstacles", 1); i++)
{
playersList.Add(CellType.Pit);
}
for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numGoals", 1f); i++)
{
playersList.Add(CellType.Goal);
}
players = playersList.ToArray();
m_Plane.transform.localScale = new Vector3(gridSize / 10.0f, 1f, gridSize / 10.0f);
m_Plane.transform.localPosition = new Vector3((gridSize - 1) / 2f, -0.5f, (gridSize - 1) / 2f);
m_Sn.transform.localScale = new Vector3(1, 1, gridSize + 2);
m_Ss.transform.localScale = new Vector3(1, 1, gridSize + 2);
m_Sn.transform.localPosition = new Vector3((gridSize - 1) / 2f, 0.0f, gridSize);
m_Ss.transform.localPosition = new Vector3((gridSize - 1) / 2f, 0.0f, -1);
m_Se.transform.localScale = new Vector3(1, 1, gridSize + 2);
m_Sw.transform.localScale = new Vector3(1, 1, gridSize + 2);
m_Se.transform.localPosition = new Vector3(gridSize, 0.0f, (gridSize - 1) / 2f);
m_Sw.transform.localPosition = new Vector3(-1, 0.0f, (gridSize - 1) / 2f);
if(m_AgentCam != null)
{
m_AgentCam.orthographicSize = (gridSize) / 2f;
m_AgentCam.transform.localPosition = new Vector3((gridSize - 1) / 2f, gridSize + 1f, (gridSize - 1) / 2f);
}
if (board == null)
{
board = new CellType[gridSize, gridSize];
}
else
{
for (var i = 0; i < gridSize; i++)
{
for (var j = 0; j < gridSize; j++)
{
board[i, j] = CellType.Empty;
}
}
}
}
public void AreaReset()
{
var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
foreach (var actor in actorObjs)
{
DestroyImmediate(actor);
}
SetEnvironment();
actorObjs.Clear();
var numbers = new HashSet<int>();
while (numbers.Count < players.Length + 1)
{
numbers.Add(Random.Range(0, gridSize * gridSize));
}
var numbersA = numbers.ToArray();
for (var i = 0; i < players.Length; i++)
{
var x = (numbersA[i]) / gridSize;
var z = (numbersA[i]) % gridSize;
var actorObj = Instantiate(m_Objects[players[i]], transform);
actorObj.transform.localPosition = new Vector3(x, -0.25f, z);
actorObjs.Add(actorObj);
board[x, z] = players[i];
}
var xA = (numbersA[players.Length]) / gridSize;
var zA = (numbersA[players.Length]) % gridSize;
trueAgent.transform.localPosition = new Vector3(xA, -0.25f, zA);
board[xA, zA] = CellType.Agent;
}
}