using System.Collections ;
using System.Collections.Generic ;
using UnityEngine ;
#endif
namespace MLAgents
{
[SerializeField]
[Tooltip("If checked, the brain will broadcast states and actions to Python.")]
[SerializeField] [ Tooltip ( "If checked, the brain will broadcast states and actions to Python." ) ]
#pragma warning disable
private bool broadcast = true ;
#pragma warning restore
{
public enum t ensorType
public enum T ensorType
{
Integer ,
FloatingPoint
public tensorType valueType ;
public TensorType valueType ;
MLAgents . Batcher brainBatcher ;
Batcher brainBatcher ;
[Tooltip("This must be the bytes file corresponding to the pretrained TensorFlow graph.")]
/// Modify only in inspector : Reference to the Graph asset
/// Modify only in inspector : Name of the previous action node
public string PreviousActionPlaceholderName = "prev_action" ;
#if ENABLE_TENSORFLOW
TFGraph graph ;
TFSession session ;
bool hasRecurrent ;
bool hasState ;
bool hasBatchSize ;
bool hasPrevAction ;
float [ , ] inputState ;
int [ ] inputPrevAction ;
List < float [ , , , ] > observationMatrixList ;
float [ , ] inputOldMemories ;
List < Texture2D > texturesHolder ;
int memorySize ;
TFGraph graph ;
TFSession session ;
bool hasRecurrent ;
bool hasState ;
bool hasBatchSize ;
bool hasPrevAction ;
float [ , ] inputState ;
int [ ] inputPrevAction ;
List < float [ , , , ] > observationMatrixList ;
float [ , ] inputOldMemories ;
List < Texture2D > texturesHolder ;
int memorySize ;
#endif
/// Reference to the brain that uses this CoreBrainInternal
{
#if ENABLE_TENSORFLOW
#if UNITY_ANDROID
// This needs to ba called only once and will raise an exception if
// there are multiple internal brains
// This needs to ba called only once and will raise an exception if
// there are multiple internal brains
try {
TensorFlowSharp . Android . NativeBinding . Init ( ) ;
}
#endif
if ( ( brainBatcher = = null )
| | ( ! broadcast ) )
{
this . brainBatcher = null ;
}
else
{
this . brainBatcher = brainBatcher ;
this . brainBatcher . SubscribeBrain ( brain . gameObject . name ) ;
}
if ( ( brainBatcher = = null )
| | ( ! broadcast ) )
{
this . brainBatcher = null ;
}
else
{
this . brainBatcher = brainBatcher ;
this . brainBatcher . SubscribeBrain ( brain . gameObject . name ) ;
}
if ( graphModel ! = null )
{
graph = new TFGraph ( ) ;
graph . Import ( graphModel . bytes ) ;
session = new TFSession ( graph ) ;
if ( graphModel ! = null )
{
// TODO: Make this a loop over a dynamic set of graph inputs
graph = new TFGraph ( ) ;
if ( ( graphScope . Length > 1 ) & & ( graphScope [ graphScope . Length - 1 ] ! = '/' ) )
{
graphScope = graphScope + '/' ;
}
graph . Import ( graphModel . bytes ) ;
if ( graph [ graphScope + BatchSizePlaceholderName ] ! = null )
{
hasBatchSize = true ;
}
session = new TFSession ( graph ) ;
if ( ( graph [ graphScope + RecurrentInPlaceholderName ] ! = null ) & &
( graph [ graphScope + RecurrentOutPlaceholderName ] ! = null ) )
{
hasRecurrent = true ;
var runner = session . GetRunner ( ) ;
runner . Fetch ( graph [ graphScope + "memory_size" ] [ 0 ] ) ;
var networkOutput = runner . Run ( ) [ 0 ] . GetValue ( ) ;
memorySize = ( int ) networkOutput ;
}
// TODO: Make this a loop over a dynamic set of graph inputs
if ( graph [ graphScope + VectorObservationPlacholderName ] ! = null )
{
hasState = true ;
}
if ( ( graphScope . Length > 1 ) & & ( graphScope [ graphScope . Length - 1 ] ! = '/' ) )
{
graphScope = graphScope + '/' ;
if ( graph [ graphScope + PreviousActionPlaceholderName ] ! = null )
{
hasPrevAction = true ;
}
if ( graph [ graphScope + BatchSizePlaceholderName ] ! = null )
{
hasBatchSize = true ;
}
if ( ( graph [ graphScope + RecurrentInPlaceholderName ] ! = null ) & & ( graph [ graphScope + RecurrentOutPlaceholderName ] ! = null ) )
{
hasRecurrent = true ;
var runner = session . GetRunner ( ) ;
runner . Fetch ( graph [ graphScope + "memory_size" ] [ 0 ] ) ;
var networkOutput = runner . Run ( ) [ 0 ] . GetValue ( ) ;
memorySize = ( int ) networkOutput ;
}
if ( graph [ graphScope + VectorObservationPlacholderName ] ! = null )
{
hasState = true ;
}
if ( graph [ graphScope + PreviousActionPlaceholderName ] ! = null )
{
hasPrevAction = true ;
}
}
observationMatrixList = new List < float [ , , , ] > ( ) ;
texturesHolder = new List < Texture2D > ( ) ;
observationMatrixList = new List < float [ , , , ] > ( ) ;
texturesHolder = new List < Texture2D > ( ) ;
/// Uses the stored information to run the tensorflow graph and generate
#if ENABLE_TENSORFLOW
if ( brainBatcher ! = null )
{
brainBatcher . SendBrainInfo ( brain . gameObject . name , agentInfo ) ;
}
int currentBatchSize = agentInfo . Count ( ) ;
List < Agent > agentList = agentInfo . Keys . ToList ( ) ;
if ( currentBatchSize = = 0 )
{
return ;
}
if ( brainBatcher ! = null )
{
brainBatcher . SendBrainInfo ( brain . gameObject . name , agentInfo ) ;
}
int currentBatchSize = agentInfo . Count ( ) ;
List < Agent > agentList = agentInfo . Keys . ToList ( ) ;
if ( currentBatchSize = = 0 )
{
return ;
}
// Create the state tensor
if ( hasState )
{
int stateLength = 1 ;
if ( brain . brainParameters . vectorObservationSpaceType = = SpaceType . continuous )
// Create the state tensor
if ( hasState )
int stateLength = 1 ;
}
inputState =
new float [ currentBatchSize , stateLength * brain . brainParameters . numStackedVectorObservations ] ;
inputState =
new float [ currentBatchSize , stateLength * brain . brainParameters . numStackedVectorObservations ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
{
List < float > state_list = agentInfo [ agent ] . stackedVectorObservation ;
for ( int j =
0 ; j < stateLength * brain . brainParameters . numStackedVectorObservations ; j + + )
var i = 0 ;
foreach ( Agent agent in agentList )
inputState [ i , j ] = state_list [ j ] ;
List < float > stateList = agentInfo [ agent ] . stackedVectorObservation ;
for ( int j =
0 ;
j < stateLength * brain . brainParameters . numStackedVectorObservations ;
j + + )
{
inputState [ i , j ] = stateList [ j ] ;
}
i + + ;
i + + ;
}
// Create the state tensor
if ( hasPrevAction )
{
inputPrevAction = new int [ currentBatchSize ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
// Create the state tensor
if ( hasPrevAction )
float [ ] action_list = agentInfo [ agent ] . storedVectorActions ;
inputPrevAction [ i ] = Mathf . FloorToInt ( action_list [ 0 ] ) ;
i + + ;
inputPrevAction = new int [ currentBatchSize ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
{
float [ ] actionList = agentInfo [ agent ] . storedVectorActions ;
inputPrevAction [ i ] = Mathf . FloorToInt ( actionList [ 0 ] ) ;
i + + ;
}
}
observationMatrixList . Clear ( ) ;
for ( int observationIndex =
0 ; observationIndex < brain . brainParameters . cameraResolutions . Count ( ) ; observationIndex + + ) {
texturesHolder . Clear ( ) ;
foreach ( Agent agent in agentList ) {
texturesHolder . Add ( agentInfo [ agent ] . visualObservations [ observationIndex ] ) ;
}
observationMatrixList . Add (
BatchVisualObservations ( texturesHolder , brain . brainParameters . cameraResolutions [ observationIndex ] . blackAndWhite ) ) ;
}
// Create the recurrent tensor
if ( hasRecurrent )
{
// Need to have variable memory size
inputOldMemories = new float [ currentBatchSize , memorySize ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
observationMatrixList . Clear ( ) ;
for ( int observationIndex =
0 ;
observationIndex < brain . brainParameters . cameraResolutions . Length ;
observationIndex + + )
float [ ] m = agentInfo [ agent ] . memories . ToArray ( ) ;
for ( int j = 0 ; j < m . Count ( ) ; j + + )
texturesHolder . Clear ( ) ;
foreach ( Agent agent in agentList )
inputOldMemories [ i , j ] = m [ j ] ;
texturesHolder . Add ( agentInfo [ agent ] . visualObservations [ observationIndex ] ) ;
i + + ;
observationMatrixList . Add (
BatchVisualObservations ( texturesHolder ,
brain . brainParameters . cameraResolutions [ observationIndex ] . blackAndWhite ) ) ;
}
// Create the recurrent tensor
if ( hasRecurrent )
{
// Need to have variable memory size
inputOldMemories = new float [ currentBatchSize , memorySize ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
{
float [ ] m = agentInfo [ agent ] . memories . ToArray ( ) ;
for ( int j = 0 ; j < m . Length ; j + + )
{
inputOldMemories [ i , j ] = m [ j ] ;
}
var runner = session . GetRunner ( ) ;
try
{
runner . Fetch ( graph [ graphScope + ActionPlaceholderName ] [ 0 ] ) ;
}
catch
{
throw new UnityAgentsException ( string . Format ( @"The node {0} could not be found. Please make sure the graphScope {1} is correct" ,
graphScope + ActionPlaceholderName , graphScope ) ) ;
}
i + + ;
}
}
if ( hasBatchSize )
{
runner . AddInput ( graph [ graphScope + BatchSizePlaceholderName ] [ 0 ] , new int [ ] { currentBatchSize } ) ;
}
foreach ( TensorFlowAgentPlaceholder placeholder in graphPlaceholders )
{
var runner = session . GetRunner ( ) ;
if ( placeholder . valueType = = TensorFlowAgentPlaceholder . tensorType . FloatingPoint )
{
runner . AddInput ( graph [ graphScope + placeholder . name ] [ 0 ] , new float [ ] { Random . Range ( placeholder . minValue , placeholder . maxValue ) } ) ;
}
else if ( placeholder . valueType = = TensorFlowAgentPlaceholder . tensorType . Integer )
{
runner . AddInput ( graph [ graphScope + placeholder . name ] [ 0 ] , new int [ ] { Random . Range ( ( int ) placeholder . minValue , ( int ) placeholder . maxValue + 1 ) } ) ;
}
runner . Fetch ( graph [ graphScope + ActionPlaceholderName ] [ 0 ] ) ;
throw new UnityAgentsException ( string . Format ( @ "One of the Tensorflow placeholder cound nout be found.
In brain { 0 } , there are no { 1 } placeholder named { 2 } . ",
brain . gameObject . name , placeholder . valueType . ToString ( ) , graphScope + placeholder . name ) ) ;
throw new UnityAgentsException ( string . Format (
@"The node {0} could not be found. Please make sure the graphScope {1} is correct" ,
graphScope + ActionPlaceholderName , graphScope ) ) ;
}
// Create the state tensor
if ( hasState )
{
if ( brain . brainParameters . vectorObservationSpaceType = = SpaceType . discrete )
if ( hasBatchSize )
var discreteInputState = new int [ currentBatchSize , 1 ] ;
for ( int i = 0 ; i < currentBatchSize ; i + + )
runner . AddInput ( graph [ graphScope + BatchSizePlaceholderName ] [ 0 ] , new int [ ] { currentBatchSize } ) ;
}
foreach ( TensorFlowAgentPlaceholder placeholder in graphPlaceholders )
{
try
discreteInputState [ i , 0 ] = ( int ) inputState [ i , 0 ] ;
if ( placeholder . valueType = = TensorFlowAgentPlaceholder . TensorType . FloatingPoint )
{
runner . AddInput ( graph [ graphScope + placeholder . name ] [ 0 ] ,
new float [ ] { Random . Range ( placeholder . minValue , placeholder . maxValue ) } ) ;
}
else if ( placeholder . valueType = = TensorFlowAgentPlaceholder . TensorType . Integer )
{
runner . AddInput ( graph [ graphScope + placeholder . name ] [ 0 ] ,
new int [ ] { Random . Range ( ( int ) placeholder . minValue , ( int ) placeholder . maxValue + 1 ) } ) ;
}
runner . AddInput ( graph [ graphScope + VectorObservationPlacholderName ] [ 0 ] , discreteInputState ) ;
catch
{
throw new UnityAgentsException ( string . Format (
@ "One of the Tensorflow placeholder cound nout be found.
In brain { 0 } , there are no { 1 } placeholder named { 2 } . ",
brain . gameObject . name , placeholder . valueType . ToString ( ) , graphScope + placeholder . name ) ) ;
}
else
// Create the state tensor
if ( hasState )
}
// Create the previous action tensor
if ( hasPrevAction )
{
runner . AddInput ( graph [ graphScope + PreviousActionPlaceholderName ] [ 0 ] , inputPrevAction ) ;
}
// Create the previous action tensor
if ( hasPrevAction )
{
runner . AddInput ( graph [ graphScope + PreviousActionPlaceholderName ] [ 0 ] , inputPrevAction ) ;
}
// Create the observation tensors
for ( int obs_number =
0 ; obs_number < brain . brainParameters . cameraResolutions . Length ; obs_number + + )
{
runner . AddInput ( graph [ graphScope + VisualObservationPlaceholderName [ obs_number ] ] [ 0 ] , observationMatrixList [ obs_number ] ) ;
}
// Create the observation tensors
for ( int obsNumber =
0 ;
obsNumber < brain . brainParameters . cameraResolutions . Length ;
obsNumber + + )
{
runner . AddInput ( graph [ graphScope + VisualObservationPlaceholderName [ obsNumber ] ] [ 0 ] ,
observationMatrixList [ obsNumber ] ) ;
}
if ( hasRecurrent )
{
runner . AddInput ( graph [ graphScope + "sequence_length" ] [ 0 ] , 1 ) ;
runner . AddInput ( graph [ graphScope + RecurrentInPlaceholderName ] [ 0 ] , inputOldMemories ) ;
runner . Fetch ( graph [ graphScope + RecurrentOutPlaceholderName ] [ 0 ] ) ;
}
if ( hasRecurrent )
{
runner . AddInput ( graph [ graphScope + "sequence_length" ] [ 0 ] , 1 ) ;
runner . AddInput ( graph [ graphScope + RecurrentInPlaceholderName ] [ 0 ] , inputOldMemories ) ;
runner . Fetch ( graph [ graphScope + RecurrentOutPlaceholderName ] [ 0 ] ) ;
}
TFTensor [ ] networkOutput ;
try
{
networkOutput = runner . Run ( ) ;
}
catch ( TFException e )
{
string errorMessage = e . Message ;
TFTensor [ ] networkOutput ;
errorMessage =
string . Format ( @"The tensorflow graph needs an input for {0} of type {1}" ,
e . Message . Split ( new string [ ] { "Node: " } , 0 ) [ 1 ] . Split ( '=' ) [ 0 ] ,
e . Message . Split ( new string [ ] { "dtype=" } , 0 ) [ 1 ] . Split ( ',' ) [ 0 ] ) ;
networkOutput = runner . Run ( ) ;
finally
catch ( TFException e )
throw new UnityAgentsException ( errorMessage ) ;
string errorMessage = e . Message ;
try
{
errorMessage =
$@"The tensorflow graph needs an input for {e.Message.Split(new string[] {" Node : "}, 0)[1].Split('=')[0]} of type {e.Message.Split(new string[] {" dtype = "}, 0)[1].Split(',')[0]}" ;
}
finally
{
throw new UnityAgentsException ( errorMessage ) ;
}
}
// Create the recurrent tensor
if ( hasRecurrent )
{
float [ , ] recurrent_tensor = networkOutput [ 1 ] . GetValue ( ) as float [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
// Create the recurrent tensor
if ( hasRecurrent )
var m = new float [ memorySize ] ;
for ( int j = 0 ; j < memorySize ; j + + )
float [ , ] recurrentTensor = networkOutput [ 1 ] . GetValue ( ) as float [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
m [ j ] = recurrent_tensor [ i , j ] ;
var m = new float [ memorySize ] ;
for ( int j = 0 ; j < memorySize ; j + + )
{
m [ j ] = recurrentTensor [ i , j ] ;
}
agent . UpdateMemoriesAction ( m . ToList ( ) ) ;
i + + ;
agent . UpdateMemoriesAction ( m . ToList ( ) ) ;
i + + ;
}
if ( brain . brainParameters . vectorActionSpaceType = = SpaceType . continuous )
{
var output = networkOutput [ 0 ] . GetValue ( ) as float [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
if ( brain . brainParameters . vectorActionSpaceType = = SpaceType . continuous )
var a = new float [ brain . brainParameters . vectorActionSize ] ;
for ( int j = 0 ; j < brain . brainParameters . vectorActionSize ; j + + )
var output = networkOutput [ 0 ] . GetValue ( ) as float [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
a [ j ] = output [ i , j ] ;
var a = new float [ brain . brainParameters . vectorActionSize ] ;
for ( int j = 0 ; j < brain . brainParameters . vectorActionSize ; j + + )
{
a [ j ] = output [ i , j ] ;
}
agent . UpdateVectorAction ( a ) ;
i + + ;
agent . UpdateVectorAction ( a ) ;
i + + ;
}
else if ( brain . brainParameters . vectorActionSpaceType = = SpaceType . discrete )
{
long [ , ] output = networkOutput [ 0 ] . GetValue ( ) as long [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
else if ( brain . brainParameters . vectorActionSpaceType = = SpaceType . discrete )
var a = new float [ 1 ] { ( float ) ( output [ i , 0 ] ) } ;
agent . UpdateVectorAction ( a ) ;
i + + ;
long [ , ] output = networkOutput [ 0 ] . GetValue ( ) as long [ , ] ;
var i = 0 ;
foreach ( Agent agent in agentList )
{
var a = new float [ 1 ] { ( float ) ( output [ i , 0 ] ) } ;
agent . UpdateVectorAction ( a ) ;
i + + ;
}
}
#else
public void OnInspector ( )
{
#if ENABLE_TENSORFLOW && UNITY_EDITOR
EditorGUILayout . LabelField ( "" , GUI . skin . horizontalSlider ) ;
broadcast = EditorGUILayout . Toggle ( new GUIContent ( "Broadcast" ,
"If checked, the brain will broadcast states and actions to Python." ) , broadcast ) ;
EditorGUILayout . LabelField ( "" , GUI . skin . horizontalSlider ) ;
broadcast = EditorGUILayout . Toggle ( new GUIContent ( "Broadcast" ,
"If checked, the brain will broadcast states and actions to Python." ) , broadcast ) ;
var serializedBrain = new SerializedObject ( this ) ;
GUILayout . Label ( "Edit the Tensorflow graph parameters here" ) ;
var tfGraphModel = serializedBrain . FindProperty ( "graphModel" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . ObjectField ( tfGraphModel ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
var serializedBrain = new SerializedObject ( this ) ;
GUILayout . Label ( "Edit the Tensorflow graph parameters here" ) ;
var tfGraphModel = serializedBrain . FindProperty ( "graphModel" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . ObjectField ( tfGraphModel ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
if ( graphModel = = null )
{
EditorGUILayout . HelpBox ( "Please provide a tensorflow graph as a bytes file." , MessageType . Error ) ;
}
if ( graphModel = = null )
{
EditorGUILayout . HelpBox ( "Please provide a tensorflow graph as a bytes file." , MessageType . Error ) ;
}
graphScope =
EditorGUILayout . TextField ( new GUIContent ( "Graph Scope" , "If you set a scope while training your tensorflow model, " +
"all your placeholder name will have a prefix. You must specify that prefix here." ) , graphScope ) ;
graphScope =
EditorGUILayout . TextField ( new GUIContent ( "Graph Scope" ,
"If you set a scope while training your tensorflow model, " +
"all your placeholder name will have a prefix. You must specify that prefix here." ) , graphScope ) ;
if ( BatchSizePlaceholderName = = "" )
{
BatchSizePlaceholderName = "batch_size" ;
}
if ( BatchSizePlaceholderName = = "" )
{
BatchSizePlaceholderName = "batch_size" ;
}
BatchSizePlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Batch Size Node Name" , "If the batch size is one of " +
"the inputs of your graph, you must specify the name if the placeholder here." ) , BatchSizePlaceholderName ) ;
if ( VectorObservationPlacholderName = = "" )
{
VectorObservationPlacholderName = "state" ;
}
VectorObservationPlacholderName =
EditorGUILayout . TextField ( new GUIContent ( "Vector Observation Node Name" , "If your graph uses the state as an input, " +
"you must specify the name if the placeholder here." ) , VectorObservationPlacholderName ) ;
if ( RecurrentInPlaceholderName = = "" )
{
RecurrentInPlaceholderName = "recurrent_in" ;
}
RecurrentInPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Recurrent Input Node Name" , "If your graph uses a " +
"recurrent input / memory as input and outputs new recurrent input / memory, " +
"you must specify the name if the input placeholder here." ) , RecurrentInPlaceholderName ) ;
if ( RecurrentOutPlaceholderName = = "" )
{
RecurrentOutPlaceholderName = "recurrent_out" ;
}
RecurrentOutPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Recurrent Output Node Name" , " If your graph uses a " +
"recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if " +
"the output placeholder here." ) , RecurrentOutPlaceholderName ) ;
BatchSizePlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Batch Size Node Name" , "If the batch size is one of " +
"the inputs of your graph, you must specify the name if the placeholder here." ) ,
BatchSizePlaceholderName ) ;
if ( VectorObservationPlacholderName = = "" )
{
VectorObservationPlacholderName = "state" ;
}
if ( brain . brainParameters . cameraResolutions ! = null )
{
if ( brain . brainParameters . cameraResolutions . Count ( ) > 0 )
VectorObservationPlacholderName =
EditorGUILayout . TextField ( new GUIContent ( "Vector Observation Node Name" ,
"If your graph uses the state as an input, " +
"you must specify the name if the placeholder here." ) , VectorObservationPlacholderName ) ;
if ( RecurrentInPlaceholderName = = "" )
if ( VisualObservationPlaceholderName = = null )
{
VisualObservationPlaceholderName =
new string [ brain . brainParameters . cameraResolutions . Count ( ) ] ;
}
if ( VisualObservationPlaceholderName . Count ( ) ! = brain . brainParameters . cameraResolutions . Count ( ) )
{
VisualObservationPlaceholderName =
new string [ brain . brainParameters . cameraResolutions . Count ( ) ] ;
}
for ( int obs_number =
0 ; obs_number < brain . brainParameters . cameraResolutions . Count ( ) ; obs_number + + )
RecurrentInPlaceholderName = "recurrent_in" ;
}
RecurrentInPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Recurrent Input Node Name" , "If your graph uses a " +
"recurrent input / memory as input and outputs new recurrent input / memory, " +
"you must specify the name if the input placeholder here." ) ,
RecurrentInPlaceholderName ) ;
if ( RecurrentOutPlaceholderName = = "" )
{
RecurrentOutPlaceholderName = "recurrent_out" ;
}
RecurrentOutPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Recurrent Output Node Name" , " If your graph uses a " +
"recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if " +
"the output placeholder here." ) ,
RecurrentOutPlaceholderName ) ;
if ( brain . brainParameters . cameraResolutions ! = null )
{
if ( brain . brainParameters . cameraResolutions . Count ( ) > 0 )
if ( ( VisualObservationPlaceholderName [ obs_number ] = = "" ) | | ( VisualObservationPlaceholderName [ obs_number ] = = null ) )
if ( VisualObservationPlaceholderName = = null )
VisualObservationPlaceholderName =
new string [ brain . brainParameters . cameraResolutions . Count ( ) ] ;
}
VisualObservationPlaceholderName [ obs_number ] =
"visual_observation_" + obs_number ;
if ( VisualObservationPlaceholderName . Count ( ) ! = brain . brainParameters . cameraResolutions . Count ( ) )
{
VisualObservationPlaceholderName =
new string [ brain . brainParameters . cameraResolutions . Count ( ) ] ;
for ( int obs_number =
0 ;
obs_number < brain . brainParameters . cameraResolutions . Count ( ) ;
obs_number + + )
{
if ( ( VisualObservationPlaceholderName [ obs_number ] = = "" ) | |
( VisualObservationPlaceholderName [ obs_number ] = = null ) )
{
VisualObservationPlaceholderName [ obs_number ] =
"visual_observation_" + obs_number ;
}
}
var opn = serializedBrain . FindProperty ( "VisualObservationPlaceholderName" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . PropertyField ( opn , true ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
var opn = serializedBrain . FindProperty ( "VisualObservationPlaceholderName" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . PropertyField ( opn , true ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
}
if ( ActionPlaceholderName = = "" )
{
ActionPlaceholderName = "action" ;
}
ActionPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Action Node Name" , "Specify the name of the " +
"placeholder corresponding to the actions of the brain in your graph. If the action space type is " +
"continuous, the output must be a one dimensional tensor of float of length Action Space Size, " +
"if the action space type is discrete, the output must be a one dimensional tensor of int " +
"of length 1." ) , ActionPlaceholderName ) ;
if ( ActionPlaceholderName = = "" )
{
ActionPlaceholderName = "action" ;
}
ActionPlaceholderName =
EditorGUILayout . TextField ( new GUIContent ( "Action Node Name" , "Specify the name of the " +
"placeholder corresponding to the actions of the brain in your graph. If the action space type is " +
"continuous, the output must be a one dimensional tensor of float of length Action Space Size, " +
"if the action space type is discrete, the output must be a one dimensional tensor of int " +
"of length 1." ) , ActionPlaceholderName ) ;
var tfPlaceholders = serializedBrain . FindProperty ( "graphPlaceholders" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . PropertyField ( tfPlaceholders , true ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
var tfPlaceholders = serializedBrain . FindProperty ( "graphPlaceholders" ) ;
serializedBrain . Update ( ) ;
EditorGUILayout . PropertyField ( tfPlaceholders , true ) ;
serializedBrain . ApplyModifiedProperties ( ) ;
#endif
#if !ENABLE_TENSORFLOW && UNITY_EDITOR
EditorGUILayout . HelpBox (
return result ;
}
}
}
Arthur Juliani
7 年前