public void ApplyDiscreteActionOutputLegacy()
{
var actionSpec = ActionSpec.MakeDiscrete(2, 3);
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 5 },
data = new Tensor(
2,
5,
new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
};
var alloc = new TensorCachingAllocator();
var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc);
var agentIds = new List <int>()
{
0, 1
};
// Dictionary from AgentId to Action
var actionDict = new Dictionary <int, ActionBuffers>()
{
{ 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty }
};
applier.Apply(inputTensor, agentIds, actionDict);
Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
alloc.Dispose();
}
public void ApplyHybridActionOutputLegacy()
{
var actionSpec = new ActionSpec(3, new[] { 2, 3 });
var continuousInputTensor = new TensorProxy()
{
shape = new long[] { 2, 3 },
data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
};
var discreteInputTensor = new TensorProxy()
{
shape = new long[] { 2, 8 },
data = new Tensor(
2,
5,
new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
};
var continuousApplier = new ContinuousActionOutputApplier(actionSpec);
var alloc = new TensorCachingAllocator();
var discreteApplier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc);
var agentIds = new List <int>()
{
0, 1
};
// Dictionary from AgentId to Action
var actionDict = new Dictionary <int, ActionBuffers>()
{
{ 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty }
};
continuousApplier.Apply(continuousInputTensor, agentIds, actionDict);
discreteApplier.Apply(discreteInputTensor, agentIds, actionDict);
Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
alloc.Dispose();
}
public void TestDiscreteApply()
{
var actionSpec = ActionSpec.MakeDiscrete(3, 2);
const float smallLogProb = -1000.0f;
const float largeLogProb = -1.0f;
var logProbs = new TensorProxy
{
data = new Tensor(
2,
5,
new[]
{
smallLogProb, smallLogProb, largeLogProb, // Agent 0, branch 0
smallLogProb, largeLogProb, // Agent 0, branch 1
largeLogProb, smallLogProb, smallLogProb, // Agent 1, branch 0
largeLogProb, smallLogProb, // Agent 1, branch 1
}),
valueType = TensorProxy.TensorType.FloatingPoint
};
var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 2020, null);
var agentIds = new List <int> {
42, 1337
};
var actionBuffers = new Dictionary <int, ActionBuffers>();
actionBuffers[42] = new ActionBuffers(actionSpec);
actionBuffers[1337] = new ActionBuffers(actionSpec);
applier.Apply(logProbs, agentIds, actionBuffers);
Assert.AreEqual(2, actionBuffers[42].DiscreteActions[0]);
Assert.AreEqual(1, actionBuffers[42].DiscreteActions[1]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[0]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[1]);
}
