Add comprehensive logging utilities for DQN training with best practices:
- LoggingConfig: Configurable log levels, intervals, and sampling rates
- MetricsAggregator: Windowed statistics (mean, std_dev) for training metrics
- SampledLogger: Rate-limited logging for high-frequency events
Key features:
- Structured logging with tracing crate (info/debug/trace hierarchy)
- 23 unit tests for full coverage
- Integration with existing DQN training pipeline
Bug fixes:
- Fix u8 overflow in prioritized_replay.rs test (500 > u8::MAX)
- Fix GradStore assertion in residual.rs (no is_empty method)
- Fix Tensor::get() Option/Result handling in quantile_regression.rs
- Fix Device PartialEq comparison in ensemble_network.rs
Documentation:
- Add Rust logging best practices guide for ML training
- Add DQN logging analysis and design summary
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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