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130 行
4.5 KiB
130 行
4.5 KiB
// using System.Collections;
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// using System.Collections.Generic;
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// using UnityEngine;
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// using Unity.MLAgents.Sensors;
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// using System;
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// using System.Linq;
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// /// <summary>
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// /// A simple example of a SensorComponent.
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// /// This should be added to the same GameObject as the BasicController
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// /// </summary>
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// public class AttentionSensorComponent : SensorComponent
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// {
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// public int ObservableSize;
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// public int MaxNumObservables;
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// /// <summary>
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// /// Creates a BasicSensor.
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// /// </summary>
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// /// <returns></returns>
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// public override ISensor CreateSensor()
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// {
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// return new AttentionSensor(transform, ObservableSize, MaxNumObservables);
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// }
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// /// <inheritdoc/>
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// public override int[] GetObservationShape()
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// {
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// return new[] { MaxNumObservables, ObservableSize, 1};
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// }
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// }
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// /// <summary>
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// /// Simple Sensor implementation that uses a one-hot encoding of the Agent's
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// /// position as the observation.
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// /// </summary>
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// public class AttentionSensor : ISensor
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// {
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// int m_ObservableSize;
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// int m_MaxNumObservables;
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// float[] m_ObservationBuffer;
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// int m_CurrentNumObservables;
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// Transform m_AgentTransform;
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// public AttentionSensor(Transform AgentTransform, int ObservableSize, int MaxNumObservables)
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// {
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// m_ObservableSize = ObservableSize;
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// m_MaxNumObservables = MaxNumObservables;
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// m_AgentTransform = AgentTransform;
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// m_ObservationBuffer = new float[m_ObservableSize * m_MaxNumObservables];
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// m_CurrentNumObservables = 0;
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// }
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// /// <summary>
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// /// Generate the observations for the sensor.
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// /// In this case, the observations are all 0 except for a 1 at the position of the agent.
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// /// </summary>
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// /// <param name="output"></param>
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// public int Write(ObservationWriter writer)
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// {
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// for (int i = 0; i < m_ObservableSize * m_MaxNumObservables; i++){
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// writer[i] = m_ObservationBuffer[i];
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// }
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// return m_ObservableSize * m_MaxNumObservables;
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// }
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// public byte[] GetCompressedObservation()
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// {
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// return new byte[0];
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// }
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// public int[] GetObservationShape()
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// {
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// return new[] { m_MaxNumObservables, m_ObservableSize,1 };
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// }
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// /// <inheritdoc/>
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// public void Update() {
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// Reset();
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// var bullets = m_AgentTransform.parent.GetComponentsInChildren<Bullet>();
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// // Sort by closest :
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// Array.Sort(bullets , (a, b) => Vector3.Distance(a.transform.position, m_AgentTransform.position) - Vector3.Distance(b.transform.position, m_AgentTransform.position) > 0 ? 1 : -1);
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// // foreach (Bullet b in bullets)
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// // {
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// // b.transform.localScale = 0.5f * new Vector3(1,1,1);
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// // }
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// foreach (Bullet b in bullets)
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// {
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// if (m_CurrentNumObservables >= m_MaxNumObservables){
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// break;
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// }
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// m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 0] = (b.transform.position.x - m_AgentTransform.parent.position.x) / 10f;
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// m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 1] = (b.transform.position.z - m_AgentTransform.parent.position.z) / 10f;
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// //m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 0] = (b.transform.position.x - m_AgentTransform.position.x) / 10f;
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// //m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 1] = (b.transform.position.z - m_AgentTransform.position.z) / 10f;
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// m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 2] = b.transform.forward.x;
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// m_ObservationBuffer[m_CurrentNumObservables * m_ObservableSize + 3] = b.transform.forward.z;
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// m_CurrentNumObservables += 1;
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// // b.transform.localScale = 1f* new Vector3(1,1,1);
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// }
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// }
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// /// <inheritdoc/>
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// public void Reset() {
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// m_CurrentNumObservables = 0;
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// Array.Clear(m_ObservationBuffer, 0, m_ObservationBuffer.Length);
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// }
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// public SensorCompressionType GetCompressionType()
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// {
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// return SensorCompressionType.None;
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// }
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// /// <summary>
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// /// Accessor for the name of the sensor.
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// /// </summary>
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// /// <returns>Sensor name.</returns>
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// public string GetName()
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// {
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// return "AttentionSensor";
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// }
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// }
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