feat(spiel_bot): network with mlp and resnet

spiel_bot/src/network/mod.rs

@@ -0,0 +1,64 @@
+//! Neural network abstractions for policy-value learning.
+//!
+//! # Trait
+//!
+//! [`PolicyValueNet<B>`] is the single trait that all network architectures
+//! implement.  It takes an observation tensor and returns raw policy logits
+//! plus a tanh-squashed scalar value estimate.
+//!
+//! # Architectures
+//!
+//! | Module | Description | Default hidden |
+//! |--------|-------------|----------------|
+//! | [`MlpNet`] | 2-hidden-layer MLP — fast to train, good baseline | 256 |
+//! | [`ResNet`] | 4-residual-block network — stronger long-term | 512 |
+//!
+//! # Backend convention
+//!
+//! * **Inference / self-play** — use `NdArray<f32>` (no autodiff overhead).
+//! * **Training** — use `Autodiff<NdArray<f32>>` so Burn can differentiate
+//!   through the forward pass.
+//!
+//! Both modes use the exact same struct; only the type-level backend changes:
+//!
+//! ```rust,ignore
+//! use burn::backend::{Autodiff, NdArray};
+//! type InferBackend = NdArray<f32>;
+//! type TrainBackend = Autodiff<NdArray<f32>>;
+//!
+//! let infer_net = MlpNet::<InferBackend>::new(&MlpConfig::default(), &Default::default());
+//! let train_net = MlpNet::<TrainBackend>::new(&MlpConfig::default(), &Default::default());
+//! ```
+//!
+//! # Output shapes
+//!
+//! Given a batch of `B` observations of size `obs_size`:
+//!
+//! | Output | Shape | Range |
+//! |--------|-------|-------|
+//! | `policy_logits` | `[B, action_size]` | ℝ (unnormalised) |
+//! | `value` | `[B, 1]` | (-1, 1) via tanh |
+//!
+//! Callers are responsible for masking illegal actions in `policy_logits`
+//! before passing to softmax.
+
+pub mod mlp;
+pub mod resnet;
+
+pub use mlp::{MlpConfig, MlpNet};
+pub use resnet::{ResNet, ResNetConfig};
+
+use burn::{module::Module, tensor::backend::Backend, tensor::Tensor};
+
+/// A neural network that produces a policy and a value from an observation.
+///
+/// # Shapes
+/// - `obs`: `[batch, obs_size]`
+/// - policy output: `[batch, action_size]`  — raw logits (no softmax applied)
+/// - value output:  `[batch, 1]`            — tanh-squashed ∈ (-1, 1)
+/// Note: `Sync` is intentionally absent — Burn's `Module` internally uses
+/// `OnceCell` for lazy parameter initialisation, which is not `Sync`.
+/// Use an `Arc<Mutex<N>>` wrapper if cross-thread sharing is needed.
+pub trait PolicyValueNet<B: Backend>: Module<B> + Send + 'static {
+    fn forward(&self, obs: Tensor<B, 2>) -> (Tensor<B, 2>, Tensor<B, 2>);
+}