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: 1,618 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 32 33 34 35 36 37 38 39 40 41 42 43 | #if MLA_INPUT_SYSTEM
using Unity.MLAgents.Actuators;
using UnityEngine;
using UnityEngine.InputSystem;
using UnityEngine.InputSystem.LowLevel;
namespace Unity.MLAgents.Extensions.Input
{
/// <summary>
/// Translates data from any control that extends from <see cref="InputControl{Vector2}"/>.
/// </summary>
public class Vector2InputActionAdaptor : IRLActionInputAdaptor
{
/// <inheritdoc cref="IRLActionInputAdaptor.GetActionSpecForInputAction"/>
public ActionSpec GetActionSpecForInputAction(InputAction action)
{
// TODO create the action spec based on what controls back the action
return ActionSpec.MakeContinuous(2);
}
/// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action,
InputControl control,
ActionSpec actionSpec,
in ActionBuffers actionBuffers)
{
var x = actionBuffers.ContinuousActions[0];
var y = actionBuffers.ContinuousActions[1];
control.WriteValueIntoEvent(new Vector2(x, y), eventPtr);
}
/// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>
public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
{
var value = action.ReadValue<Vector2>();
var continuousActions = actionBuffers.ContinuousActions;
continuousActions[0] = value.x;
continuousActions[1] = value.y;
}
}
}
#endif // MLA_INPUT_SYSTEM
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