refactor(ml): extract DQN module into ml-dqn crate (task 6)

Move 53 DQN source files + gpu_replay_buffer from ml into standalone
ml-dqn crate. The ml crate's dqn module is now a thin re-export layer
(`pub use ml_dqn::*`) plus two bridge files (trainable_adapter.rs,
stress_testing.rs) that depend on ml-internal types.

Key changes:
- ml-dqn: 45 modules, 334 tests, compiles standalone
- ml: depends on ml-dqn, re-exports via dqn/mod.rs
- DQN.config: pub(crate) → pub for cross-crate access
- DQNAgent checkpoint methods moved into ml-dqn
- gpu_replay_buffer moved from cuda_pipeline/ to ml-dqn
- 8 dead-code files removed (never declared in mod.rs)

Net: -30,648 lines from ml crate. Workspace: 0 errors.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

crates/ml/Cargo.toml

@@ -69,6 +69,7 @@ colored = "2.1"  # Terminal color output for evaluation reports
 
 # Internal workspace crates
 ml-core.workspace = true
+ml-dqn.workspace = true
 config.workspace = true
 common = { workspace = true, features = ["questdb"] }
 risk = { path = "../risk" }