feat(*): Add self-play RL training pipeline with PPO trainer, in-game GDScript policy inference, and bot opponent support in Match mode

Game/addons/godot_rl_agents/onnx/csharp/ONNXInference.cs

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+using Godot;
+using Microsoft.ML.OnnxRuntime;
+using Microsoft.ML.OnnxRuntime.Tensors;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace GodotONNX
+{
+	/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/ONNXInference/*'/>
+	public partial class ONNXInference : GodotObject
+	{
+
+		private InferenceSession session;
+		/// <summary>
+		/// Path to the ONNX model. Use Initialize to change it. 
+		/// </summary>
+		private string modelPath;
+		private int batchSize;
+
+		private SessionOptions SessionOpt;
+
+		/// <summary>
+		/// init function
+		/// </summary>
+		/// <param name="Path"></param>
+		/// <param name="BatchSize"></param>
+		/// <returns>Returns the output size of the model</returns>
+		public int Initialize(string Path, int BatchSize)
+		{
+			modelPath = Path;
+			batchSize = BatchSize;
+			SessionOpt = SessionConfigurator.MakeConfiguredSessionOptions();
+			session = LoadModel(modelPath);
+			return session.OutputMetadata["output"].Dimensions[1];
+		}
+
+
+		/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Run/*'/>
+		public Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> RunInference(Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> obs, int state_ins)
+		{
+			//Current model: Any (Godot Rl Agents)
+			//Expects a tensor of shape [batch_size, input_size] type float for any output of the agents observation dictionary and a tensor of shape [batch_size] type float named state_ins
+
+			var modelInputsList = new List<NamedOnnxValue>
+			{
+				NamedOnnxValue.CreateFromTensor("state_ins", new DenseTensor<float>(new float[] { state_ins }, new int[] { batchSize }))
+			};
+			foreach (var key in obs.Keys)
+			{
+				var subObs = obs[key];
+				// Fill the input tensors for each key of the observation
+				// create span of observation from specific inputSize
+				var obsData = new float[subObs.Count]; //There's probably a better way to do this
+				for (int i = 0; i < subObs.Count; i++)
+				{
+					obsData[i] = subObs[i];
+				}
+				modelInputsList.Add(
+					NamedOnnxValue.CreateFromTensor(key, new DenseTensor<float>(obsData, new int[] { batchSize, subObs.Count }))
+				);
+			}
+
+			IReadOnlyCollection<string> outputNames = new List<string> { "output", "state_outs" }; //ONNX is sensible to these names, as well as the input names
+
+			IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results;
+			//We do not use "using" here so we get a better exception explaination later
+			try
+			{
+				results = session.Run(modelInputsList, outputNames);
+			}
+			catch (OnnxRuntimeException e)
+			{
+				//This error usually means that the model is not compatible with the input, beacause of the input shape (size)
+				GD.Print("Error at inference: ", e);
+				return null;
+			}
+			//Can't convert IEnumerable<float> to Variant, so we have to convert it to an array or something
+			Godot.Collections.Dictionary<string, Godot.Collections.Array<float>> output = new Godot.Collections.Dictionary<string, Godot.Collections.Array<float>>();
+			DisposableNamedOnnxValue output1 = results.First();
+			DisposableNamedOnnxValue output2 = results.Last();
+			Godot.Collections.Array<float> output1Array = new Godot.Collections.Array<float>();
+			Godot.Collections.Array<float> output2Array = new Godot.Collections.Array<float>();
+
+			foreach (float f in output1.AsEnumerable<float>())
+			{
+				output1Array.Add(f);
+			}
+
+			foreach (float f in output2.AsEnumerable<float>())
+			{
+				output2Array.Add(f);
+			}
+
+			output.Add(output1.Name, output1Array);
+			output.Add(output2.Name, output2Array);
+
+			//Output is a dictionary of arrays, ex: { "output" : [0.1, 0.2, 0.3, 0.4, ...], "state_outs" : [0.5, ...]}
+			results.Dispose();
+			return output;
+		}
+		/// <include file='docs/ONNXInference.xml' path='docs/members[@name="ONNXInference"]/Load/*'/>
+		public InferenceSession LoadModel(string Path)
+		{
+			using Godot.FileAccess file = FileAccess.Open(Path, Godot.FileAccess.ModeFlags.Read);
+			byte[] model = file.GetBuffer((int)file.GetLength());
+			//file.Close(); file.Dispose(); //Close the file, then dispose the reference.
+			return new InferenceSession(model, SessionOpt); //Load the model
+		}
+		public void FreeDisposables()
+		{
+			session.Dispose();
+			SessionOpt.Dispose();
+		}
+	}
+}