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。
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using UnityEngine;
using UnityEngine.Serialization;
namespace MLAgents
{
/// <summary>
/// Implemetation of the Player Brain. Inherits from the base class Brain. Allows the user to
/// manually select decisions for linked agents by creating a mapping from keys presses to
/// actions.
/// You can use Player Brains to control a "teacher" Agent that trains other Agents during
/// imitation learning. You can also use Player Brains to test your Agents and environment
/// before training agents with reinforcement learning.
/// </summary>
[CreateAssetMenu(fileName = "NewPlayerBrain", menuName = "ML-Agents/Player Brain")]
public class PlayerBrain : Brain
{
[System.Serializable]
public struct DiscretePlayerAction
{
public KeyCode key;
public int branchIndex;
public int value;
}
[System.Serializable]
public struct KeyContinuousPlayerAction
{
public KeyCode key;
public int index;
public float value;
}
[System.Serializable]
public struct AxisContinuousPlayerAction
{
public string axis;
public int index;
public float scale;
}
/// Contains the mapping from input to continuous actions
[SerializeField]
[FormerlySerializedAs("continuousPlayerActions")]
[Tooltip("The list of keys and the value they correspond to for continuous control.")]
public KeyContinuousPlayerAction[] keyContinuousPlayerActions;
/// Contains the mapping from input to continuous actions
[SerializeField]
[Tooltip("The list of axis actions.")]
public AxisContinuousPlayerAction[] axisContinuousPlayerActions;
/// Contains the mapping from input to discrete actions
[SerializeField]
[Tooltip("The list of keys and the value they correspond to for discrete control.")]
public DiscretePlayerAction[] discretePlayerActions;
protected override void Initialize() {}
/// Uses the continuous inputs or dicrete inputs of the player to
/// decide action
protected override void DecideAction()
{
if (brainParameters.vectorActionSpaceType == SpaceType.Continuous)
{
foreach (var agent in m_Agents)
{
var action = new float[brainParameters.vectorActionSize[0]];
foreach (var cha in keyContinuousPlayerActions)
{
if (Input.GetKey(cha.key))
{
action[cha.index] = cha.value;
}
}
foreach (var axisAction in axisContinuousPlayerActions)
{
var axisValue = Input.GetAxis(axisAction.axis);
axisValue *= axisAction.scale;
if (Mathf.Abs(axisValue) > 0.0001)
{
action[axisAction.index] = axisValue;
}
}
agent.UpdateVectorAction(action);
}
}
else
{
foreach (var agent in m_Agents)
{
var action = new float[brainParameters.vectorActionSize.Length];
foreach (var dha in discretePlayerActions)
{
if (Input.GetKey(dha.key))
{
action[dha.branchIndex] = dha.value;
}
}
agent.UpdateVectorAction(action);
}
}
}
}
}