Reinforcement Learning
ml-agents
TensorBoard
ONNX
Pyramids
deep-reinforcement-learning
ML-Agents-Pyramids
Instructions to use AnnaMats/ppo-Pyramids-Training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use AnnaMats/ppo-Pyramids-Training with ml-agents:
mlagents-load-from-hf --repo-id="AnnaMats/ppo-Pyramids-Training" --local-dir="./download: string[]s"
- Notebooks
- Google Colab
- Kaggle
File size: 1,062 Bytes
05c9ac2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | using NUnit.Framework;
using Unity.MLAgents.Sensors;
namespace Unity.MLAgents.Tests
{
[TestFixture]
public class CompressionSpecTests
{
[Test]
public void TestIsTrivialMapping()
{
Assert.IsTrue(CompressionSpec.Default().IsTrivialMapping());
var spec = new CompressionSpec(SensorCompressionType.PNG, null);
Assert.AreEqual(spec.IsTrivialMapping(), true);
spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 0, 0 });
Assert.AreEqual(spec.IsTrivialMapping(), true);
spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 1, 2, 3, 4 });
Assert.AreEqual(spec.IsTrivialMapping(), true);
spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 1, 2, 3, 4, -1, -1 });
Assert.AreEqual(spec.IsTrivialMapping(), false);
spec = new CompressionSpec(SensorCompressionType.PNG, new[] { 0, 0, 0, 1, 1, 1 });
Assert.AreEqual(spec.IsTrivialMapping(), false);
}
}
}
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