Reinforcement Learning Platform Overview

class CustomerServiceReward:
    def __init__(self):
        self.criteria = {
            'resolution': 0.35,      # Did it solve the problem?
            'empathy': 0.25,         # Was it understanding?
            'clarity': 0.20,         # Was it clear?
            'efficiency': 0.10,      # Was it concise?
            'policy_compliance': 0.10 # Did it follow rules?
        }
    
    def calculate(self, response, context):
        scores = {}
        
        # Resolution scoring
        scores['resolution'] = self.check_resolution(response, context)
        
        # Empathy detection
        scores['empathy'] = self.measure_empathy(response)
        
        # Clarity assessment
        scores['clarity'] = self.evaluate_clarity(response)
        
        # Length efficiency
        scores['efficiency'] = min(1.0, 50 / len(response.split()))
        
        # Policy adherence
        scores['policy_compliance'] = self.check_policies(response, context)
        
        # Weighted sum
        total = sum(scores[k] * self.criteria[k] for k in scores)
        
        return total, scores  # Return total and breakdown

# GRPO Configuration
actor_rollout:
  ref:
    rollout:
      n: 8  # Generate 8 responses per prompt (critical for GRPO)

data:
  train_batch_size: 32  # Number of prompts per batch

actor_rollout_ref:
  actor:
    ppo_mini_batch_size: 256  # Mini-batch for weight updates
    ppo_epochs: 4  # Update epochs per trajectory set
    clip_ratio: 0.2  # GRPO clip range for stable training
    
    # GRPO-specific settings
    use_kl_loss: true  # Enable KL regularization
    kl_loss_coef: 0.001  # KL penalty strength
    kl_loss_type: "kl"  # Options: kl, abs, mse, low_var_kl, full
    
    # Loss aggregation
    loss_agg_mode: "token-mean"  # Stable for long responses

algorithm:
  adv_estimator: "grpo"  # Use GRPO instead of GAE

from stateset.rl import GRPOTrainer, RewardFunction

# Define reward function
class ServiceReward(RewardFunction):
    def compute(self, prompt, response, context):
        score = 0.0
        
        # Check resolution
        if self.resolves_issue(prompt, response):
            score += 2.0
        
        # Measure empathy
        empathy_score = self.empathy_detector(response)
        score += empathy_score * 0.5
        
        # Length penalty
        if len(response.split()) > 150:
            score -= 0.3
        
        # Policy compliance
        if self.follows_policies(response, context):
            score += 0.5
        
        return score

# Configure trainer
trainer = GRPOTrainer(
    model="gpt-3.5-turbo",
    reward_function=ServiceReward(),
    config={
        "rollout_n": 8,
        "batch_size": 32,
        "epochs": 3,
        "clip_ratio": 0.2,
        "use_kl_loss": True
    }
)

# Train on your data
dataset = load_customer_service_data()
trained_model = trainer.train(dataset)

# Real-time metrics
trainer.on_step = lambda metrics: print(f"""
Step {metrics.step}:
  Average Reward: {metrics.avg_reward:.3f}
  Reward Std: {metrics.reward_std:.3f}
  KL Divergence: {metrics.kl_div:.4f}
  Response Quality: {metrics.quality_score:.2%}
""")

# Memory-efficient training
trainer = GRPOTrainer(
    gradient_checkpointing=True,  # Trade compute for memory
    mixed_precision="fp16",       # Faster training
    gradient_accumulation_steps=4 # Larger effective batch size
)

# Distributed training for scale
trainer = GRPOTrainer(
    distributed=True,
    num_gpus=4,
    strategy="ddp"
)

# Measure improvement
baseline_model = load_model("base")
grpo_model = load_model("grpo_trained")

metrics = evaluate_models(baseline_model, grpo_model, test_set)

print(f"""
Performance Improvement:
- Task Success: +{metrics.success_delta:.1%}
- User Satisfaction: +{metrics.satisfaction_delta:.1%}
- Response Quality: +{metrics.quality_delta:.1%}
- Efficiency: +{metrics.efficiency_delta:.1%}
""")

class MultiObjectiveReward:
    def __init__(self, objectives):
        self.objectives = objectives
    
    def compute(self, response, context):
        scores = {}
        for name, (weight, function) in self.objectives.items():
            scores[name] = function(response, context) * weight
        
        # Pareto optimization
        if self.is_pareto_optimal(scores):
            bonus = 0.2
        else:
            bonus = 0
        
        return sum(scores.values()) + bonus

# Start with easy tasks, gradually increase difficulty
curriculum = [
    {"difficulty": "easy", "epochs": 1, "reward_scale": 1.0},
    {"difficulty": "medium", "epochs": 2, "reward_scale": 0.8},
    {"difficulty": "hard", "epochs": 3, "reward_scale": 0.6}
]

for stage in curriculum:
    model = trainer.train(
        data=filter_by_difficulty(data, stage["difficulty"]),
        epochs=stage["epochs"],
        reward_scale=stage["reward_scale"]
    )