2df1ea92e1
BREAKING CHANGES: - Removed orphaned dqn.rs monolithic trainer (4,975 lines) - Removed orphaned dqn_ensemble.rs module (816 lines) - Removed orphaned tft.rs and tft_complete_int8_integration_test.rs - TFT trainer split into modular directory structure DQN Module Refactoring: - Split trainers/dqn.rs into modular structure (config.rs, statistics.rs, trainer.rs) - Fixed hyperopt 39D search space (continuous params only) - Boolean flags (use_dueling, use_double_dqn, use_per, use_noisy_nets) are now FIXED architectural decisions - use_distributional defaults to false (Candle BUG #36 - scatter_add gradient issues) Clean Module Structure: - ml/src/trainers/dqn/ directory with proper mod.rs exports - ml/src/trainers/tft/ directory with config.rs, types.rs, model.rs, trainer.rs, tests.rs - All P0 features validated: TD-error clamping, batch diversity, LR scheduler, priority staleness Documentation: - Added comprehensive docs in docs/codebase-cleanup/ - ADR-001 for DQN refactoring decisions - Rainbow DQN component matrix and quick reference guides Build Status: Compiles with zero errors 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
156 lines
4.4 KiB
Markdown
156 lines
4.4 KiB
Markdown
# WAVE 28.11: DQNConfig Default Implementation
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## Summary
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Added `Default` implementation for `DQNConfig` struct in `/home/jgrusewski/Work/foxhunt/ml/src/dqn/dqn.rs`.
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## Changes Made
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### File: `/home/jgrusewski/Work/foxhunt/ml/src/dqn/dqn.rs`
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Added comprehensive `Default` implementation between the struct definition (line 146) and the existing `impl DQNConfig` block (line 195).
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**Location**: Lines 148-193
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### Default Values Chosen
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The defaults are optimized for trading DQN with sensible production-ready parameters:
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#### Core Architecture
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- `state_dim: 54` - Standard feature dimension
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- `num_actions: 45` - FactoredAction space
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- `hidden_dims: vec![256, 256]` - Two hidden layers
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#### Learning Parameters
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- `learning_rate: 1e-4` - Conservative learning rate
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- `gamma: 0.99` - Standard discount factor
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- `gradient_clip_norm: 1.0` - Gradient clipping threshold
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#### Exploration
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- `epsilon_start: 1.0` - Start with full exploration
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- `epsilon_end: 0.01` - Minimal exploration at end
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- `epsilon_decay: 0.995` - Gradual decay
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#### Replay Buffer
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- `replay_buffer_capacity: 100_000` - Large capacity
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- `batch_size: 64` - Standard batch size
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- `min_replay_size: 1000` - Minimum before training
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#### Target Updates
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- `target_update_freq: 1000` - Hard update frequency
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- `tau: 0.005` - Soft update coefficient
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- `use_soft_updates: true` - Enable Polyak averaging
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#### Rainbow DQN Features
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- `use_double_dqn: true` - Enable Double DQN
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- `use_huber_loss: true` - Use Huber loss
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- `huber_delta: 1.0` - Huber loss threshold
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- `warmup_steps: 1000` - Warmup before training
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- `n_steps: 1` - Single-step returns (conservative)
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#### Prioritized Experience Replay (PER)
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- `use_per: true` - Enable PER
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- `per_alpha: 0.6` - Prioritization exponent
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- `per_beta_start: 0.4` - Initial importance sampling weight
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- `per_beta_max: 1.0` - Maximum beta value
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- `per_beta_annealing_steps: 100_000` - Annealing schedule
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#### Dueling Networks
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- `use_dueling: true` - Enable dueling architecture
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- `dueling_hidden_dim: 128` - Advantage stream hidden dim
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#### Distributional RL (C51)
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- `use_distributional: false` - Disabled by default
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- `num_atoms: 51` - Distribution atoms
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- `v_min: -10.0` - Minimum value
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- `v_max: 10.0` - Maximum value
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#### Noisy Networks
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- `use_noisy_nets: false` - Disabled by default
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- `noisy_sigma_init: 0.5` - Initial noise std
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#### Q-Value Clipping (BUG #37 Fix)
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- `enable_q_value_clipping: true` - Enable clipping
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- `q_value_clip_min: -100.0` - Minimum Q-value
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- `q_value_clip_max: 100.0` - Maximum Q-value
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#### Early Stopping (WAVE 23)
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- `gradient_collapse_multiplier: 2.0` - Learning-rate aware threshold
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- `gradient_collapse_patience: 100` - Epochs before stopping
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#### Trading Parameters
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- `initial_capital: 100_000.0` - Starting capital
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- `leaky_relu_alpha: 0.01` - LeakyReLU negative slope
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## Verification
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All 41 struct fields are covered in the Default implementation:
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- state_dim
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- num_actions
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- hidden_dims
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- learning_rate
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- gamma
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- epsilon_start
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- epsilon_end
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- epsilon_decay
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- replay_buffer_capacity
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- batch_size
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- min_replay_size
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- target_update_freq
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- use_double_dqn
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- use_huber_loss
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- huber_delta
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- leaky_relu_alpha
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- gradient_clip_norm
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- tau
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- use_soft_updates
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- warmup_steps
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- n_steps
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- initial_capital
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- use_per
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- per_alpha
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- per_beta_start
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- per_beta_max
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- per_beta_annealing_steps
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- use_dueling
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- dueling_hidden_dim
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- use_distributional
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- num_atoms
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- v_min
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- v_max
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- use_noisy_nets
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- noisy_sigma_init
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- enable_q_value_clipping
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- q_value_clip_min
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- q_value_clip_max
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- gradient_collapse_multiplier
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- gradient_collapse_patience
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## Notes
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1. The defaults are **conservative and production-ready**
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2. Rainbow features are selectively enabled (Double DQN, Huber, PER, Dueling)
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3. More experimental features (Distributional, Noisy Nets) are disabled by default
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4. Q-value clipping is enabled to prevent explosions (BUG #37 fix)
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5. Early stopping with gradient collapse detection is configured (WAVE 23)
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## Usage
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```rust
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// Create config with sensible defaults
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let config = DQNConfig::default();
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// Or customize specific fields
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let config = DQNConfig {
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learning_rate: 1e-3,
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use_distributional: true,
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..DQNConfig::default()
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};
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```
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## Compilation Status
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The Default implementation itself is syntactically correct and complete.
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Note: There are pre-existing compilation errors in the ml crate related to type mismatches between `ml::dqn::dqn::DQNConfig` and `ml::dqn::agent::DQNConfig`. These are separate issues not introduced by this change.
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