use burn::prelude::{Backend, Tensor};
use rlevo_core::action::ContinuousAction;
use rlevo_core::base::{Action, TensorConversionError, TensorConvertible};
use serde::{Deserialize, Serialize};
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub struct SwimmerAction(pub [f32; 2]);
impl SwimmerAction {
#[must_use]
pub const fn new(joint1: f32, joint2: f32) -> Self {
Self([joint1, joint2])
}
}
impl Action<1> for SwimmerAction {
fn shape() -> [usize; 1] {
[2]
}
fn is_valid(&self) -> bool {
self.0.iter().all(|v| v.is_finite() && v.abs() <= 1.0)
}
}
impl ContinuousAction<1> for SwimmerAction {
fn as_slice(&self) -> &[f32] {
&self.0
}
fn clip(&self, min: f32, max: f32) -> Self {
Self([self.0[0].clamp(min, max), self.0[1].clamp(min, max)])
}
fn from_slice(values: &[f32]) -> Self {
Self([values[0], values[1]])
}
fn random() -> Self {
Self([
rand::random::<f32>() * 2.0 - 1.0,
rand::random::<f32>() * 2.0 - 1.0,
])
}
}
impl<B: Backend> TensorConvertible<1, B> for SwimmerAction {
fn to_tensor(&self, device: &<B as burn::tensor::backend::BackendTypes>::Device) -> Tensor<B, 1> {
Tensor::from_floats(self.0, device)
}
fn from_tensor(tensor: Tensor<B, 1>) -> Result<Self, TensorConversionError> {
let data = tensor.into_data();
let slice = data.as_slice::<f32>().map_err(|e| TensorConversionError {
message: format!("expected f32 action tensor: {e:?}"),
})?;
if slice.len() != 2 {
return Err(TensorConversionError {
message: format!("expected 2 action elements, got {}", slice.len()),
});
}
Ok(Self([slice[0], slice[1]]))
}
}