# PPO Best Hyperparameters
# Source: Hyperopt Trial #1 (Pod bpxgh10c5ocus5, EUR-IS-1, RTX A4000)
# Date: 2025-11-01
# Objective Score: 2.4023 (best of 63 trials)
# Duration: 14.3 minutes
# Cost: $0.06
# Dataset: ES_FUT_180d.parquet

# Learning Rates (ASYMMETRIC - CRITICAL)
# Policy network requires ultra-conservative LR to prevent catastrophic forgetting
# Value network can handle aggressive LR for fast convergence
learning_rate = 1.0e-4           # Deprecated: use actor/critic rates instead
actor_learning_rate = 1.0e-6     # Policy LR (ultra-conservative)
critic_learning_rate = 0.001     # Value LR (aggressive, 1000x higher)

# PPO Algorithm Parameters
clip_epsilon = 0.1126            # PPO clip range (conservative vs 0.2 default)
vf_coef = 0.5                    # Value loss coefficient (balanced)
ent_coef = 0.006142              # Entropy coefficient (low exploration)
gae_lambda = 0.95                # GAE lambda (default)
gamma = 0.99                     # Discount factor (default)

# Training Configuration
batch_size = 64                  # Batch size (standard)
minibatch_size = 64              # Mini-batch size
rollout_steps = 2048             # Steps per rollout
epochs = 10000                   # Training epochs (production)

# Early Stopping Configuration
early_stopping_enabled = true
min_value_loss_improvement_pct = 2.0
min_explained_variance = 0.4
plateau_window = 30
min_epochs_before_stopping = 50

# Performance Notes
# - Trial #1 achieved best objective (2.4023) among 63 trials
# - Policy LR cluster: 0.7e-6 to 2.5e-6 (all top 5 trials)
# - Value LR range: 0.0009 to 0.0012 (tight variance)
# - Using single learning_rate 0.001 caused loss stagnation at 1.158-1.159 (Pod 0hczpx9nj1ub88)
# - Asymmetric learning rates (33x ratio) are REQUIRED for PPO convergence

# Deployment
# - Production deployment: deploy_ppo_production_corrected.sh
# - Expected duration: 30-90 minutes on RTX A4000
# - Expected cost: $0.12-$0.38

# Related Files
# - CLAUDE.md: Recent Updates (lines 9-47)
# - PPO_PARAMETERS_QUICK_REF.md: Complete hyperopt analysis
# - ml/src/trainers/ppo.rs: Trainer implementation