f946dcd952
WAVE 22: All examples, benchmarks, and data loaders updated Files Modified (41 files): - DQN examples: 7 files (train_dqn, evaluate_dqn, validate_dqn, etc.) - PPO examples: 6 files (train_ppo, continuous_ppo, benchmark_ppo, etc.) - TFT examples: 9 files (train_tft, validate_tft, benchmark_tft, etc.) - MAMBA-2 examples: 3 files (train_mamba2, verify_dimensions, etc.) - Benchmarks: 5 files (cuda_speedup, weight_caching, future_decoder, etc.) - Data loaders: 7 files (parquet_utils, dbn_sequence_loader, tlob_loader, etc.) - Integration: 4 files (load_parquet_data, streaming loaders, etc.) Key Changes: - state_dim: 225 → 54 (DQN, PPO) - input_dim: 225 → 54 (TFT) - d_model: 225 → 54 (MAMBA-2) - Memory: 1.8KB → 0.43KB per vector (76% reduction) - All tensor shapes updated: (batch, 225) → (batch, 54) Agents Deployed: 5 parallel agents Validation: cargo check PASSING Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
110 lines
3.7 KiB
Rust
110 lines
3.7 KiB
Rust
//! Test DQN Initialization Non-Determinism
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//!
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//! Creates a DQN model and prints initial Q-values to verify
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//! that network weights are randomly initialized (not deterministic).
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//!
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//! # Usage
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//!
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//! ```bash
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//! # Run 3 times and compare Q-values
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//! cargo run -p ml --example test_dqn_init --release --features cuda
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//! cargo run -p ml --example test_dqn_init --release --features cuda
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//! cargo run -p ml --example test_dqn_init --release --features cuda
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//! ```
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use anyhow::Result;
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use candle_core::{Device, Tensor};
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use ml::dqn::{RewardSystem, WorkingDQN, WorkingDQNConfig};
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fn main() -> Result<()> {
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// Initialize tracing
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tracing_subscriber::fmt()
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.with_max_level(tracing::Level::DEBUG)
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.init();
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println!("=== DQN Initialization Test ===\n");
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// Create DQN config
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let config = WorkingDQNConfig {
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state_dim: 54, // 54 features (Wave 21 feature reduction)
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hidden_dims: vec![256, 128, 64],
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num_actions: 3,
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learning_rate: 0.0001,
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gamma: 0.99,
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epsilon_start: 1.0,
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epsilon_end: 0.05,
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epsilon_decay: 0.995,
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replay_buffer_capacity: 10000,
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batch_size: 32,
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min_replay_size: 1000,
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target_update_freq: 10000,
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use_double_dqn: true,
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use_huber_loss: false,
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huber_delta: 1.0,
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gradient_clip_norm: 10.0,
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leaky_relu_alpha: 0.01,
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tau: 0.001,
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use_soft_updates: false,
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warmup_steps: 1000,
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temperature_start: 1.0,
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temperature_min: 0.1,
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temperature_decay: 0.995,
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target_temperature_fraction: 0.75,
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variance_multiplier: 0.5,
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use_adaptive_temperature: false,
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loss_improvement_threshold: 0.999,
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plateau_window: 10,
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temp_increase_factor: 1.05,
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temperature_slow_decay: 0.998,
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reward_system: RewardSystem::Elite,
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};
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println!("Creating DQN model...");
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let dqn = WorkingDQN::new(config)?;
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println!("✓ DQN model created\n");
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// Create a test state (all zeros)
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let device = dqn.device();
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let test_state = Tensor::zeros((1, 128), candle_core::DType::F32, device)?;
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println!("Computing initial Q-values for zero state...");
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let q_values = dqn.forward(&test_state)?;
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// Extract Q-values
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let q_vec = q_values.squeeze(0)?.to_vec1::<f32>()?;
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println!("\n=== INITIAL Q-VALUES (Step 0) ===");
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println!(" BUY (Action 0): {:+.6}", q_vec[0]);
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println!(" SELL (Action 1): {:+.6}", q_vec[1]);
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println!(" HOLD (Action 2): {:+.6}", q_vec[2]);
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println!("\n=== Q-Value Differences ===");
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println!(" HOLD - BUY: {:+.6}", q_vec[2] - q_vec[0]);
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println!(" HOLD - SELL: {:+.6}", q_vec[2] - q_vec[1]);
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println!(" BUY - SELL: {:+.6}", q_vec[0] - q_vec[1]);
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// Check for deterministic initialization (209% HOLD bias)
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let hold_bias = (q_vec[2] - q_vec[0]) / q_vec[0].abs();
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println!("\n=== Bias Analysis ===");
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println!(" HOLD bias: {:.1}%", hold_bias * 100.0);
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if hold_bias.abs() > 1.5 {
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println!(
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" ⚠️ WARNING: Large HOLD bias detected (>{:.0}%)",
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hold_bias.abs() * 100.0
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);
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} else {
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println!(" ✓ HOLD bias within acceptable range (<150%)");
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}
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println!("\n=== VALIDATION ===");
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println!("Run this example 3 times in parallel:");
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println!(" cargo run -p ml --example test_dqn_init --release --features cuda &");
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println!(" cargo run -p ml --example test_dqn_init --release --features cuda &");
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println!(" cargo run -p ml --example test_dqn_init --release --features cuda &");
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println!(" wait");
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println!("\nSUCCESS: If Q-values are DIFFERENT across runs");
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println!("FAILURE: If Q-values are IDENTICAL across runs");
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Ok(())
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}
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