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using Unity.InferenceEngine;
using NUnit.Framework;
using UnityEngine;
using UnityEngine.TestTools;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Policies;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Utils.Tests;
namespace Unity.MLAgents.Tests
{
internal class OverflowSensor : ISensor
{
readonly string m_Name;
readonly int m_Size;
public OverflowSensor(string name, int size)
{
m_Name = name;
m_Size = size;
}
public ObservationSpec GetObservationSpec()
{
return ObservationSpec.Vector(m_Size);
}
public int Write(ObservationWriter writer)
{
for (var i = 0; i < m_Size; i++)
writer[i] = i + 1f;
return m_Size;
}
public byte[] GetCompressedObservation() { return null; }
public CompressionSpec GetCompressionSpec() { return new CompressionSpec(SensorCompressionType.None); }
public string GetName() { return m_Name; }
public void Update() { }
public void Reset() { }
}
[TestFixture]
public class EditModeTestInternalBrainTensorGenerator
{
[SetUp]
public void SetUp()
{
if (Academy.IsInitialized)
{
Academy.Instance.Dispose();
}
}
static List<TestAgent> GetFakeAgents(ObservableAttributeOptions observableAttributeOptions = ObservableAttributeOptions.Ignore)
{
var goA = new GameObject("goA");
var bpA = goA.AddComponent<BehaviorParameters>();
bpA.BrainParameters.VectorObservationSize = 3;
bpA.BrainParameters.NumStackedVectorObservations = 1;
bpA.ObservableAttributeHandling = observableAttributeOptions;
var agentA = goA.AddComponent<TestAgent>();
var goB = new GameObject("goB");
var bpB = goB.AddComponent<BehaviorParameters>();
bpB.BrainParameters.VectorObservationSize = 3;
bpB.BrainParameters.NumStackedVectorObservations = 1;
bpB.ObservableAttributeHandling = observableAttributeOptions;
var agentB = goB.AddComponent<TestAgent>();
var agents = new List<TestAgent> { agentA, agentB };
foreach (var agent in agents)
{
agent.LazyInitialize();
}
agentA.collectObservationsSensor.AddObservation(new Vector3(1, 2, 3));
agentB.collectObservationsSensor.AddObservation(new Vector3(4, 5, 6));
var infoA = new AgentInfo
{
storedActions = new ActionBuffers(null, new[] { 1, 2 }),
discreteActionMasks = null,
};
var infoB = new AgentInfo
{
storedActions = new ActionBuffers(null, new[] { 3, 4 }),
discreteActionMasks = new[] { true, false, false, false, false },
};
agentA._Info = infoA;
agentB._Info = infoB;
return agents;
}
[Test]
public void Construction()
{
var mem = new Dictionary<int, List<float>>();
var tensorGenerator = new TensorGenerator(0, mem);
Assert.IsNotNull(tensorGenerator);
}
[Test]
public void GenerateBatchSize()
{
var inputTensor = new TensorProxy();
const int batchSize = 4;
var generator = new BatchSizeGenerator();
generator.Generate(inputTensor, batchSize, null);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[0], batchSize);
}
[Test]
public void GenerateSequenceLength()
{
var inputTensor = new TensorProxy();
const int batchSize = 4;
var generator = new SequenceLengthGenerator();
generator.Generate(inputTensor, batchSize, null);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[0], 1);
}
[Test]
public void GenerateVectorObservation()
{
var inputTensor = new TensorProxy
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new int[] { 2, 4 }
};
const int batchSize = 4;
var agentInfos = GetFakeAgents(ObservableAttributeOptions.ExamineAll);
var generator = new ObservationGenerator();
generator.AddSensorIndex(0); // ObservableAttribute (size 1)
generator.AddSensorIndex(1); // TestSensor (size 0)
generator.AddSensorIndex(2); // TestSensor (size 0)
generator.AddSensorIndex(3); // VectorSensor (size 3)
var agent0 = agentInfos[0];
var agent1 = agentInfos[1];
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair { agentInfo = agent0._Info, sensors = agent0.sensors },
new AgentInfoSensorsPair { agentInfo = agent1._Info, sensors = agent1.sensors },
};
generator.Generate(inputTensor, batchSize, inputs);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[0, 1], 1);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[0, 3], 3);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[1, 1], 4);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[1, 3], 6);
}
[Test]
public void GeneratePreviousActionInput()
{
var inputTensor = new TensorProxy
{
shape = new int[] { 2, 2 },
valueType = TensorProxy.TensorType.Integer
};
const int batchSize = 4;
var agentInfos = GetFakeAgents();
var generator = new PreviousActionInputGenerator();
var agent0 = agentInfos[0];
var agent1 = agentInfos[1];
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair { agentInfo = agent0._Info, sensors = agent0.sensors },
new AgentInfoSensorsPair { agentInfo = agent1._Info, sensors = agent1.sensors },
};
generator.Generate(inputTensor, batchSize, inputs);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[0, 0], 1);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[0, 1], 2);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[1, 0], 3);
Assert.AreEqual(((Tensor<int>)inputTensor.data)[1, 1], 4);
}
[Test]
public void GenerateActionMaskInput()
{
var inputTensor = new TensorProxy
{
shape = new int[] { 2, 5 },
valueType = TensorProxy.TensorType.FloatingPoint
};
const int batchSize = 4;
var agentInfos = GetFakeAgents();
var generator = new ActionMaskInputGenerator();
var agent0 = agentInfos[0];
var agent1 = agentInfos[1];
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair { agentInfo = agent0._Info, sensors = agent0.sensors },
new AgentInfoSensorsPair { agentInfo = agent1._Info, sensors = agent1.sensors },
};
generator.Generate(inputTensor, batchSize, inputs);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[0, 0], 1);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[0, 4], 1);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[1, 0], 0);
Assert.AreEqual((int)((Tensor<float>)inputTensor.data)[1, 4], 1);
}
[Test]
public void GenerateVectorObservation_CapacityGuardPreventsOverflow()
{
// Tensor can hold 3 floats, sensor0 fills it exactly (3),
// so the guard fires before sensor1 can write anything.
var inputTensor = new TensorProxy
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new int[] { 1, 3 }
};
var sensor0 = new OverflowSensor("sensor0", 3);
var sensor1 = new OverflowSensor("sensor1", 3);
var sensors = new List<ISensor> { sensor0, sensor1 };
var generator = new ObservationGenerator();
generator.AddSensorIndex(0);
generator.AddSensorIndex(1);
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair
{
agentInfo = new AgentInfo { done = false },
sensors = sensors
}
};
LogAssert.Expect(LogType.Warning, new System.Text.RegularExpressions.Regex("Sensor write overflow"));
generator.Generate(inputTensor, 1, inputs);
// First sensor's 3 writes land, second sensor is skipped by capacity guard
Assert.AreEqual(1f, ((Tensor<float>)inputTensor.data)[0, 0]);
Assert.AreEqual(2f, ((Tensor<float>)inputTensor.data)[0, 1]);
Assert.AreEqual(3f, ((Tensor<float>)inputTensor.data)[0, 2]);
}
[Test]
public void GenerateVectorObservation_SingleSensorOverflowIsClamped()
{
// Tensor can hold 2 floats, but sensor writes 5
var inputTensor = new TensorProxy
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new int[] { 1, 2 }
};
var sensor = new OverflowSensor("big_sensor", 5);
var sensors = new List<ISensor> { sensor };
var generator = new ObservationGenerator();
generator.AddSensorIndex(0);
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair
{
agentInfo = new AgentInfo { done = false },
sensors = sensors
}
};
// No crash — ObservationWriter bounds cap prevents the buffer overrun
generator.Generate(inputTensor, 1, inputs);
Assert.AreEqual(1f, ((Tensor<float>)inputTensor.data)[0, 0]);
Assert.AreEqual(2f, ((Tensor<float>)inputTensor.data)[0, 1]);
}
[Test]
public void GenerateVectorObservation_ExactFitNoWarning()
{
// Tensor exactly fits the sensor output — no warning should fire
var inputTensor = new TensorProxy
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new int[] { 1, 3 }
};
var sensor = new OverflowSensor("exact_sensor", 3);
var sensors = new List<ISensor> { sensor };
var generator = new ObservationGenerator();
generator.AddSensorIndex(0);
var inputs = new List<AgentInfoSensorsPair>
{
new AgentInfoSensorsPair
{
agentInfo = new AgentInfo { done = false },
sensors = sensors
}
};
generator.Generate(inputTensor, 1, inputs);
Assert.AreEqual(1f, ((Tensor<float>)inputTensor.data)[0, 0]);
Assert.AreEqual(2f, ((Tensor<float>)inputTensor.data)[0, 1]);
Assert.AreEqual(3f, ((Tensor<float>)inputTensor.data)[0, 2]);
}
}
}
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