您最多选择25个主题
主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
237 行
8.7 KiB
237 行
8.7 KiB
using System;
|
|
using System.Collections.Generic;
|
|
using System.Linq;
|
|
using Google.Protobuf;
|
|
using MLAgents.CommunicatorObjects;
|
|
using UnityEngine;
|
|
using System.Runtime.CompilerServices;
|
|
using MLAgents.Sensors;
|
|
|
|
[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor")]
|
|
[assembly: InternalsVisibleTo("Unity.ML-Agents.Editor.Tests")]
|
|
|
|
namespace MLAgents
|
|
{
|
|
internal static class GrpcExtensions
|
|
{
|
|
/// <summary>
|
|
/// Converts a AgentInfo to a protobuf generated AgentInfoActionPairProto
|
|
/// </summary>
|
|
/// <returns>The protobuf version of the AgentInfoActionPairProto.</returns>
|
|
public static AgentInfoActionPairProto ToInfoActionPairProto(this AgentInfo ai)
|
|
{
|
|
var agentInfoProto = ai.ToAgentInfoProto();
|
|
|
|
var agentActionProto = new AgentActionProto
|
|
{
|
|
VectorActions = { ai.storedVectorActions }
|
|
};
|
|
|
|
return new AgentInfoActionPairProto
|
|
{
|
|
AgentInfo = agentInfoProto,
|
|
ActionInfo = agentActionProto
|
|
};
|
|
}
|
|
|
|
/// <summary>
|
|
/// Converts a AgentInfo to a protobuf generated AgentInfoProto
|
|
/// </summary>
|
|
/// <returns>The protobuf version of the AgentInfo.</returns>
|
|
public static AgentInfoProto ToAgentInfoProto(this AgentInfo ai)
|
|
{
|
|
var agentInfoProto = new AgentInfoProto
|
|
{
|
|
Reward = ai.reward,
|
|
MaxStepReached = ai.maxStepReached,
|
|
Done = ai.done,
|
|
Id = ai.episodeId,
|
|
};
|
|
|
|
if (ai.actionMasks != null)
|
|
{
|
|
agentInfoProto.ActionMask.AddRange(ai.actionMasks);
|
|
}
|
|
|
|
return agentInfoProto;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Converts a Brain into to a Protobuf BrainInfoProto so it can be sent
|
|
/// </summary>
|
|
/// <returns>The BrainInfoProto generated.</returns>
|
|
/// <param name="bp">The instance of BrainParameter to extend.</param>
|
|
/// <param name="name">The name of the brain.</param>
|
|
/// <param name="isTraining">Whether or not the Brain is training.</param>
|
|
public static BrainParametersProto ToProto(this BrainParameters bp, string name, bool isTraining)
|
|
{
|
|
var brainParametersProto = new BrainParametersProto
|
|
{
|
|
VectorActionSize = { bp.vectorActionSize },
|
|
VectorActionSpaceType =
|
|
(SpaceTypeProto)bp.vectorActionSpaceType,
|
|
BrainName = name,
|
|
IsTraining = isTraining
|
|
};
|
|
brainParametersProto.VectorActionDescriptions.AddRange(bp.vectorActionDescriptions);
|
|
return brainParametersProto;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Convert metadata object to proto object.
|
|
/// </summary>
|
|
public static DemonstrationMetaProto ToProto(this DemonstrationMetaData dm)
|
|
{
|
|
var demoProto = new DemonstrationMetaProto
|
|
{
|
|
ApiVersion = DemonstrationMetaData.ApiVersion,
|
|
MeanReward = dm.meanReward,
|
|
NumberSteps = dm.numberExperiences,
|
|
NumberEpisodes = dm.numberEpisodes,
|
|
DemonstrationName = dm.demonstrationName
|
|
};
|
|
return demoProto;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Initialize metadata values based on proto object.
|
|
/// </summary>
|
|
public static DemonstrationMetaData ToDemonstrationMetaData(this DemonstrationMetaProto demoProto)
|
|
{
|
|
var dm = new DemonstrationMetaData
|
|
{
|
|
numberEpisodes = demoProto.NumberEpisodes,
|
|
numberExperiences = demoProto.NumberSteps,
|
|
meanReward = demoProto.MeanReward,
|
|
demonstrationName = demoProto.DemonstrationName
|
|
};
|
|
if (demoProto.ApiVersion != DemonstrationMetaData.ApiVersion)
|
|
{
|
|
throw new Exception("API versions of demonstration are incompatible.");
|
|
}
|
|
return dm;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Convert a BrainParametersProto to a BrainParameters struct.
|
|
/// </summary>
|
|
/// <param name="bpp">An instance of a brain parameters protobuf object.</param>
|
|
/// <returns>A BrainParameters struct.</returns>
|
|
public static BrainParameters ToBrainParameters(this BrainParametersProto bpp)
|
|
{
|
|
var bp = new BrainParameters
|
|
{
|
|
vectorActionSize = bpp.VectorActionSize.ToArray(),
|
|
vectorActionDescriptions = bpp.VectorActionDescriptions.ToArray(),
|
|
vectorActionSpaceType = (SpaceType)bpp.VectorActionSpaceType
|
|
};
|
|
return bp;
|
|
}
|
|
|
|
public static UnityRLInitParameters ToUnityRLInitParameters(this UnityRLInitializationInputProto inputProto)
|
|
{
|
|
return new UnityRLInitParameters
|
|
{
|
|
seed = inputProto.Seed
|
|
};
|
|
}
|
|
|
|
public static AgentAction ToAgentAction(this AgentActionProto aap)
|
|
{
|
|
return new AgentAction
|
|
{
|
|
vectorActions = aap.VectorActions.ToArray()
|
|
};
|
|
}
|
|
|
|
public static List<AgentAction> ToAgentActionList(this UnityRLInputProto.Types.ListAgentActionProto proto)
|
|
{
|
|
var agentActions = new List<AgentAction>(proto.Value.Count);
|
|
foreach (var ap in proto.Value)
|
|
{
|
|
agentActions.Add(ap.ToAgentAction());
|
|
}
|
|
return agentActions;
|
|
}
|
|
|
|
public static ObservationProto ToProto(this Observation obs)
|
|
{
|
|
ObservationProto obsProto = null;
|
|
|
|
if (obs.CompressedData != null)
|
|
{
|
|
// Make sure that uncompressed data is empty
|
|
if (obs.FloatData.Count != 0)
|
|
{
|
|
Debug.LogWarning("Observation has both compressed and uncompressed data set. Using compressed.");
|
|
}
|
|
|
|
obsProto = new ObservationProto
|
|
{
|
|
CompressedData = ByteString.CopyFrom(obs.CompressedData),
|
|
CompressionType = (CompressionTypeProto)obs.CompressionType,
|
|
};
|
|
}
|
|
else
|
|
{
|
|
var floatDataProto = new ObservationProto.Types.FloatData
|
|
{
|
|
Data = { obs.FloatData },
|
|
};
|
|
|
|
obsProto = new ObservationProto
|
|
{
|
|
FloatData = floatDataProto,
|
|
CompressionType = (CompressionTypeProto)obs.CompressionType,
|
|
};
|
|
}
|
|
|
|
obsProto.Shape.AddRange(obs.Shape);
|
|
return obsProto;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Generate an ObservationProto for the sensor using the provided WriteAdapter.
|
|
/// This is equivalent to producing an Observation and calling Observation.ToProto(),
|
|
/// but avoid some intermediate memory allocations.
|
|
/// </summary>
|
|
/// <param name="sensor"></param>
|
|
/// <param name="writeAdapter"></param>
|
|
/// <returns></returns>
|
|
public static ObservationProto GetObservationProto(this ISensor sensor, WriteAdapter writeAdapter)
|
|
{
|
|
var shape = sensor.GetObservationShape();
|
|
ObservationProto observationProto = null;
|
|
if (sensor.GetCompressionType() == SensorCompressionType.None)
|
|
{
|
|
var numFloats = sensor.ObservationSize();
|
|
var floatDataProto = new ObservationProto.Types.FloatData();
|
|
// Resize the float array
|
|
// TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
|
|
for (var i = 0; i < numFloats; i++)
|
|
{
|
|
floatDataProto.Data.Add(0.0f);
|
|
}
|
|
|
|
writeAdapter.SetTarget(floatDataProto.Data, sensor.GetObservationShape(), 0);
|
|
sensor.Write(writeAdapter);
|
|
|
|
observationProto = new ObservationProto
|
|
{
|
|
FloatData = floatDataProto,
|
|
CompressionType = (CompressionTypeProto)SensorCompressionType.None,
|
|
};
|
|
}
|
|
else
|
|
{
|
|
observationProto = new ObservationProto
|
|
{
|
|
CompressedData = ByteString.CopyFrom(sensor.GetCompressedObservation()),
|
|
CompressionType = (CompressionTypeProto)sensor.GetCompressionType(),
|
|
};
|
|
}
|
|
observationProto.Shape.AddRange(shape);
|
|
return observationProto;
|
|
}
|
|
}
|
|
}
|