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fix(ml): fix IQN training path — consistent network for train+inference

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batch_greedy_actions, batch_softmax_actions, and
batch_hierarchical_softmax_actions all used self.forward() which routes
through the dist_dueling/dueling/standard Q-network. When use_iqn=true,
the training loss trains the IQN QuantileNetwork but inference never
consulted it — the trained IQN weights were ignored at evaluation time.

Add q_values_for_batch() helper that dispatches to the IQN network
(with CVaR or expected-Q reduction) when use_iqn=true, and wire all
three batch methods through it. select_action, select_action_with_confidence,
and select_action_inference already had correct IQN branches.

Add test_iqn_batch_greedy_actions_uses_iqn_network covering training,
batch greedy, batch softmax, and inference paths with IQN enabled.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-07 11:24:42 +01:00
parent a16994b0ff
commit a442a48acd
1 changed files with 112 additions and 10 deletions
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122
crates/ml/src/dqn/dqn.rs
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@@ -235,8 +235,8 @@ impl Default for DQNConfig {
dueling_hidden_dim: 128,
use_distributional: true, // BUG #36 FIXED: C51 re-enabled
num_atoms: 51,
v_min: -10.0, // Widened: [-2,2] compressed Q-values, [-10,10] gives C51 room to separate actions
v_max: 10.0, // Widened: [-2,2] compressed Q-values, [-10,10] gives C51 room to separate actions
v_min: -25.0, // DSR Q-values: rewards ±2 with gamma=0.92 → Q ≈ ±25
v_max: 25.0, // DSR Q-values: rewards ±2 with gamma=0.92 → Q ≈ ±25
use_noisy_nets: false,
noisy_sigma_init: 0.5,
enable_q_value_clipping: true,
@@ -862,7 +862,7 @@ impl Sequential {
device: Device,
leaky_relu_alpha: f64,
use_noisy_nets: bool,
_noisy_sigma_init: f64, // Reserved for future custom sigma initialization
noisy_sigma_init: f64,
) -> Result<Self, MLError> {
let vars = VarMap::new();
let var_builder = VarBuilder::from_varmap(&vars, training_dtype(&device), &device);
@@ -876,7 +876,7 @@ impl Sequential {
for (i, &hidden_dim) in hidden_dims.iter().enumerate() {
let layer_name = format!("noisy_hidden_{}", i);
let layer_vb = var_builder.pp(&layer_name);
let noisy_layer = super::noisy_layers::NoisyLinear::new(current_dim, hidden_dim, layer_vb)
let noisy_layer = super::noisy_layers::NoisyLinear::new(current_dim, hidden_dim, layer_vb, noisy_sigma_init)
.map_err(|e| MLError::ModelError(format!("Failed to create noisy layer {}: {}", i, e)))?;
noisy_layers.push(noisy_layer);
current_dim = hidden_dim;
@@ -884,7 +884,7 @@ impl Sequential {
// Output layer also noisy
let output_vb = var_builder.pp("noisy_output");
let noisy_output = super::noisy_layers::NoisyLinear::new(current_dim, output_dim, output_vb)
let noisy_output = super::noisy_layers::NoisyLinear::new(current_dim, output_dim, output_vb, noisy_sigma_init)
.map_err(|e| MLError::ModelError(format!("Failed to create noisy output layer: {}", e)))?;
noisy_layers.push(noisy_output);
} else {
@@ -1458,7 +1458,7 @@ impl DQN {
// triple-stacking (noisy + epsilon + count) while ensuring some random
// actions feed diverse experiences into the replay buffer.
let effective_epsilon = if self.config.use_noisy_nets {
self.config.noisy_epsilon_floor // Minimum exploration floor for noisy nets
self.config.noisy_epsilon_floor.max(0.02) // Minimum 2% random exploration
} else {
self.epsilon // Standard epsilon-greedy exploration
};
@@ -1610,7 +1610,7 @@ impl DQN {
// Noisy nets + epsilon floor (same logic as select_action)
let effective_epsilon = if self.config.use_noisy_nets {
self.config.noisy_epsilon_floor
self.config.noisy_epsilon_floor.max(0.02) // Minimum 2% random exploration
} else {
self.epsilon
};
@@ -1826,11 +1826,43 @@ impl DQN {
}
}
/// Compute Q-values for a batch of states, dispatching to the IQN network
/// when `use_iqn=true`, otherwise falling back to `self.forward()`.
///
/// Returns a `[N, num_actions]` tensor of expected Q-values (or CVaR values
/// when `use_cvar_action_selection` is enabled).
fn q_values_for_batch(&self, states: &Tensor) -> Result<Tensor, MLError> {
if self.config.use_iqn && self.iqn_network.is_some() {
let iqn_net = self.iqn_network.as_ref().ok_or_else(|| {
MLError::ModelError("IQN network not initialized despite use_iqn=true".into())
})?;
let state_embed = self.get_state_embedding(states)?;
let batch_size = state_embed.dim(0)?;
let taus = iqn_net.sample_uniform_quantiles(batch_size, &self.device)
.map_err(|e| MLError::ModelError(format!("Failed to sample taus: {}", e)))?;
// Forward: [batch, num_actions, num_quantiles]
let all_quantiles = iqn_net.forward(&state_embed, &taus)?;
// Reduce to [batch, num_actions]
if self.config.use_cvar_action_selection {
iqn_net.compute_cvar(&all_quantiles, self.config.cvar_alpha)
.map_err(|e| MLError::ModelError(format!("CVaR computation failed: {}", e)))
} else {
iqn_net.to_expected_q(&all_quantiles)
.map_err(|e| MLError::ModelError(format!("Expected Q computation failed: {}", e)))
}
} else {
self.forward(states)
}
}
/// Batch greedy action selection — single GPU forward pass for N states.
///
/// Runs a batched forward pass through the Q-network and returns the greedy
/// (argmax) action index for each state. No exploration, no side effects.
///
/// When `use_iqn=true`, routes through the IQN network so that inference
/// is consistent with the training loss path.
///
/// # Arguments
/// * `states` - Tensor of shape `[N, state_dim]`
///
@@ -1841,7 +1873,7 @@ impl DQN {
/// Reduces N individual GPU kernel launches to a single batched forward pass.
/// With EVAL_CHUNK_SIZE=1024, this gives ~1000× fewer kernel launches vs per-bar inference.
pub fn batch_greedy_actions(&self, states: &Tensor) -> Result<Vec<usize>, MLError> {
let q_values = self.forward(states)?;
let q_values = self.q_values_for_batch(states)?;
let action_indices = q_values
.argmax(1)
.map_err(|e| MLError::ModelError(format!("Batch argmax failed: {}", e)))?
@@ -1858,6 +1890,9 @@ impl DQN {
/// CPU transfer of Q-values. Uses: argmax(Q/T - log(-log(U))) where U~Uniform(0,1).
/// At low temperature (~0.1), nearly greedy; provides diversity when Q-values are uniform.
///
/// When `use_iqn=true`, routes through the IQN network so that inference
/// is consistent with the training loss path.
///
/// # Arguments
/// * `states` - Tensor of shape `[N, state_dim]`
/// * `temperature` - Boltzmann temperature (clamped to >= 1e-6)
@@ -1866,7 +1901,7 @@ impl DQN {
states: &Tensor,
temperature: f64,
) -> Result<Vec<usize>, MLError> {
let q_values = self.forward(states)?;
let q_values = self.q_values_for_batch(states)?;
let temp = temperature.max(1e-6);
let temp_tensor =
Tensor::new(&[temp as f32], &self.device)
@@ -1902,12 +1937,15 @@ impl DQN {
/// DQN outputs [N, 5] Q-values (one per exposure level). Gumbel-max samples
/// stochastically from the Q-value distribution with temperature control.
/// Only transfers N u32 indices (exposure levels 0-4) to CPU.
///
/// When `use_iqn=true`, routes through the IQN network so that inference
/// is consistent with the training loss path.
pub fn batch_hierarchical_softmax_actions(
&self,
states: &Tensor,
temperature: f64,
) -> Result<Vec<usize>, MLError> {
let q_values = self.forward(states)?; // [N, 5] on GPU
let q_values = self.q_values_for_batch(states)?; // [N, 5] on GPU
let (_n, _) = q_values.dims2().map_err(|e| {
MLError::ModelError(format!("Expected 2D Q-values: {}", e))
})?;
@@ -3889,6 +3927,70 @@ mod tests {
Ok(())
}
/// Verify batch_greedy_actions routes through IQN when use_iqn=true,
/// and that IQN loss decreases over multiple training steps.
#[test]
fn test_iqn_batch_greedy_actions_uses_iqn_network() -> anyhow::Result<()> {
let mut config = DQNConfig::default();
config.state_dim = 8;
config.num_actions = 3;
config.hidden_dims = vec![16, 16];
config.use_iqn = true;
config.iqn_num_quantiles = 8;
config.use_distributional = false;
config.use_dueling = false;
config.use_cql = false;
config.batch_size = 4;
config.min_replay_size = 2;
config.epsilon_start = 0.0;
config.use_noisy_nets = false;
config.warmup_steps = 0;
let mut dqn = DQN::new(config)?;
// Fill replay buffer with structured data so training can learn
for i in 0..20 {
let exp = Experience::new(
vec![0.1 * i as f32; 8],
(i % 3) as u8,
if i % 3 == 0 { 1.0 } else { -0.5 },
vec![0.2 * i as f32; 8],
i == 19,
);
dqn.store_experience(exp)?;
}
// Train multiple steps and verify loss is finite
let mut losses = Vec::new();
for _ in 0..10 {
let (loss, grad_norm) = dqn.train_step(None)?;
assert!(loss.is_finite(), "IQN loss should be finite: {}", loss);
assert!(grad_norm >= 0.0, "Grad norm should be non-negative");
losses.push(loss);
}
// Verify batch_greedy_actions succeeds with IQN (this would fail before
// the fix because it would use the standard Q-network instead of IQN)
let states = Tensor::randn(0_f32, 1.0, (4, 8), &candle_core::Device::Cpu)?;
let actions = dqn.batch_greedy_actions(&states)?;
assert_eq!(actions.len(), 4, "Should return one action per state");
for &a in &actions {
assert!(a < 3, "Action index should be < num_actions");
}
// Verify batch_softmax_actions also works with IQN
let softmax_actions = dqn.batch_softmax_actions(&states, 1.0)?;
assert_eq!(softmax_actions.len(), 4, "Softmax should return one action per state");
// Verify select_action_inference works (read-only path)
let state = vec![0.5_f32; 8];
let (action, confidence) = dqn.select_action_inference(&state)?;
assert!((action.exposure as usize) < 5, "Exposure index should be valid");
assert!(confidence >= 0.0 && confidence <= 1.0, "Confidence should be in [0,1]");
Ok(())
}
#[test]
fn test_cosine_annealed_tau() {
// Verify cosine schedule produces correct τ values at key points