diff --git a/crates/ml/src/dqn/dqn.rs b/crates/ml/src/dqn/dqn.rs
index 4841ae4ea..476587a8a 100644
--- a/crates/ml/src/dqn/dqn.rs
+++ b/crates/ml/src/dqn/dqn.rs
@@ -22,6 +22,7 @@ use candle_nn::ops::leaky_relu;
 use candle_nn::Module;
 use candle_nn::{Linear, VarBuilder, VarMap};
 use candle_optimisers::adam::ParamsAdam;
+use candle_optimisers::Decay;
 use rand::{thread_rng, Rng};
 use serde::{Deserialize, Serialize};
 use tracing::debug;
@@ -1497,7 +1498,8 @@ impl DQN {
                 beta_1: 0.9,
                 beta_2: 0.999,
                 eps: 1.5e-4, // Rainbow DQN standard (was 1e-8)
-                weight_decay: None,
+                weight_decay: (self.config.weight_decay > 0.0)
+                    .then_some(Decay::DecoupledWeightDecay(self.config.weight_decay)),
                 amsgrad: false,
             };
 
@@ -3249,4 +3251,73 @@ mod tests {
             dqn.err()
         );
     }
+
+    /// Verify that DQNConfig.weight_decay is wired through to the Adam optimizer.
+    /// With weight_decay > 0, training a few steps should produce different final
+    /// weights than weight_decay == 0, proving L2 regularization is active.
+    #[test]
+    fn test_weight_decay_wired_to_optimizer() -> anyhow::Result<()> {
+        // Helper: run a few training steps and return the first Q-network parameter tensor
+        fn train_and_get_params(weight_decay: f64) -> anyhow::Result<Vec<f32>> {
+            let mut config = DQNConfig::emergency_safe_defaults();
+            config.state_dim = 8;
+            config.num_actions = 3;
+            config.hidden_dims = vec![16, 16];
+            config.use_iqn = false;
+            config.use_distributional = false;
+            config.use_dueling = false;
+            config.use_cql = false;
+            config.batch_size = 4;
+            config.min_replay_size = 4;
+            config.learning_rate = 1e-3; // Higher LR to make weight_decay effect visible
+            config.weight_decay = weight_decay;
+
+            let mut dqn = DQN::new(config)?;
+
+            // Fill replay buffer
+            for i in 0..10 {
+                let exp = Experience::new(
+                    vec![0.1 * i as f32; 8],
+                    (i % 3) as u8,
+                    0.5,
+                    vec![0.2 * i as f32; 8],
+                    false,
+                );
+                dqn.store_experience(exp)?;
+            }
+
+            // Run several training steps so weight_decay has a measurable effect
+            for _ in 0..20 {
+                let _ = dqn.train_step(None)?;
+            }
+
+            // Extract first parameter tensor values
+            let vars = dqn.q_network.vars().all_vars();
+            let first_var = vars.first().ok_or_else(|| {
+                anyhow::anyhow!("Q-network has no parameters")
+            })?;
+            // Cast to F32 in case mixed-precision uses BF16 weights on GPU
+            let values = first_var
+                .flatten_all()?
+                .to_dtype(candle_core::DType::F32)?
+                .to_vec1::<f32>()?;
+            Ok(values)
+        }
+
+        let params_no_decay = train_and_get_params(0.0)?;
+        let params_with_decay = train_and_get_params(1e-2)?; // Strong decay to make difference clear
+
+        // The parameters MUST differ — proving weight_decay is actually applied
+        let diff: f32 = params_no_decay
+            .iter()
+            .zip(params_with_decay.iter())
+            .map(|(a, b)| (a - b).abs())
+            .sum();
+        assert!(
+            diff > 1e-6,
+            "Weight decay should produce different parameters (total abs diff = {diff})"
+        );
+
+        Ok(())
+    }
 }