04d8802c94
Remove use_double_dqn, use_dueling, use_per, use_branching,
use_distributional, use_noisy_nets, use_huber_loss, and use_cql
from DQNConfig, DQNHyperparameters, and DqnParams structs.
These features are always enabled (Rainbow DQN standard). The boolean
flags were dead code — every constructor set them to true, and the
only code paths that set them to false were in tests that disabled
features for simplicity. With the fields removed, the features are
unconditionally active, eliminating ~490 lines of dead configuration.
Key changes:
- Struct field declarations removed from 3 core config structs
- Conditional branches (if use_X { ... } else { ... }) simplified:
dueling/branching/PER network creation is now unconditional
- Checkpoint metadata hardcodes "true" for backward compatibility
- Hyperopt search space index 11 (use_branching) fixed at 1.0
- TOML/YAML config files cleaned of removed fields
- Tests that toggled these flags updated or rewritten
45 files changed, -487 net lines. Zero new test failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
177 lines
4.6 KiB
YAML
177 lines
4.6 KiB
YAML
# Hyperparameter Tuning Configuration for Foxhunt ML Models - Agent 49 Optimized
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# Defines focused search spaces based on Agent 49 specifications
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# Global tuning settings
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global:
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optimization_direction: maximize # maximize sharpe_ratio
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pruning_enabled: true
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median_pruner:
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n_startup_trials: 5 # No pruning for first 5 trials (establish baseline)
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n_warmup_steps: 10 # Wait 10 epochs before starting to prune
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interval_steps: 5 # Check for pruning every 5 epochs
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sampler: TPE # Tree-structured Parzen Estimator (supports Bayesian optimization)
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# Model-specific search spaces (Agent 49 Specifications)
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models:
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# DQN: Deep Q-Network for Reinforcement Learning
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DQN:
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# Agent 49 specified parameters
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learning_rate:
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type: categorical
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choices: [0.00001, 0.0001, 0.001] # [1e-5, 1e-4, 1e-3]
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batch_size:
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type: categorical
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choices: [64, 128, 256]
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gamma:
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type: categorical
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choices: [0.95, 0.99, 0.999]
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# Additional DQN-specific parameters (for completeness)
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epochs:
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type: int
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low: 100
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high: 300
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step: 50
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replay_buffer_size:
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type: categorical
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choices: [50000, 100000]
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epsilon_start:
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type: float
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low: 0.95
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high: 1.0
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step: 0.05
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epsilon_end:
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type: float
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low: 0.01
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high: 0.05
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step: 0.01
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epsilon_decay_steps:
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type: int
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low: 5000
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high: 10000
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step: 1000
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target_update_frequency:
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type: categorical
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choices: [500, 1000]
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use_prioritized_replay:
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type: categorical
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choices: [true, false]
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# PPO: Proximal Policy Optimization
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PPO:
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# Agent 49 specified parameters
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learning_rate:
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type: categorical
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choices: [0.00003, 0.0001, 0.0003] # [3e-5, 1e-4, 3e-4]
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entropy_coef:
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type: categorical
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choices: [0.01, 0.05, 0.1]
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clip_ratio:
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type: categorical
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choices: [0.1, 0.2, 0.3]
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# Additional PPO-specific parameters (for completeness)
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epochs:
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type: int
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low: 100
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high: 300
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step: 50
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batch_size:
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type: categorical
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choices: [128, 256, 512]
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value_loss_coef:
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type: categorical
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choices: [0.5, 1.0]
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rollout_steps:
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type: categorical
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choices: [512, 1024, 2048]
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minibatch_size:
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type: categorical
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choices: [64, 128, 256]
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gae_lambda:
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type: categorical
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choices: [0.95, 0.97, 0.99]
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# MAMBA-2: State Space Model
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MAMBA_2:
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# Agent 49 specified parameters
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learning_rate:
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type: categorical
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choices: [0.00001, 0.0001, 0.001] # [1e-5, 1e-4, 1e-3]
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state_dim:
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type: categorical
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choices: [16, 32, 64] # State size in Agent 49 spec
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num_layers:
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type: categorical
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choices: [4, 6, 8] # Layers in Agent 49 spec
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# Additional MAMBA-2 parameters (for completeness)
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epochs:
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type: int
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low: 50
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high: 150
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step: 25
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batch_size:
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type: categorical
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choices: [64, 128, 256]
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hidden_dim:
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type: categorical
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choices: [128, 256, 512]
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dt_min:
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type: float
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low: 0.0001
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high: 0.001
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log: true
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dt_max:
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type: float
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low: 0.01
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high: 0.1
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log: true
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use_cuda_kernels:
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type: categorical
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choices: [true, false]
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# TFT: Temporal Fusion Transformer
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TFT:
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# Agent 49 specified parameters
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learning_rate:
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type: categorical
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choices: [0.00001, 0.0001, 0.001] # [1e-5, 1e-4, 1e-3]
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num_heads:
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type: categorical
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choices: [4, 8, 16] # Attention heads in Agent 49 spec
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hidden_dim:
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type: categorical
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choices: [128, 256, 512] # Hidden dim in Agent 49 spec
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# Additional TFT parameters (for completeness)
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epochs:
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type: int
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low: 50
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high: 150
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step: 25
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batch_size:
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type: categorical
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choices: [64, 128, 256]
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num_layers:
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type: categorical
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choices: [3, 4, 6]
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lookback_window:
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type: categorical
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choices: [30, 50, 100]
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forecast_horizon:
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type: categorical
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choices: [5, 10, 20]
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dropout_rate:
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type: categorical
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choices: [0.1, 0.2, 0.3]
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# Search space sizes (for reporting):
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# DQN: 3 (lr) * 3 (batch) * 3 (gamma) = 27 combinations (grid) + continuous params
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# PPO: 3 (lr) * 3 (entropy) * 3 (clip) = 27 combinations (grid) + continuous params
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# MAMBA-2: 3 (lr) * 3 (state) * 3 (layers) = 27 combinations (grid) + continuous params
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# TFT: 3 (lr) * 3 (heads) * 3 (hidden) = 27 combinations (grid) + continuous params
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#
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# Total grid combinations per model: 27
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# Recommended trials per model: 50-100 (TPE sampler explores beyond grid)
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