2.2 Reinforcement Learning with Human Feedback (RLHF)

To fully utilize the capabilities of DeepSeek R1 in our RLHF system, we've enhanced our approach:

1. Behavior Policy Network: Now uses a hybrid architecture combining DeepSeek R1 with a specialized transformer for monster behavior modeling.

2. Human Feedback Collection: Expanded to include more nuanced feedback on monster behaviors, storylines, and game balance.

3. Reward Modeling: Incorporates DeepSeek R1's reasoning capabilities to better interpret and model human preferences.

4. Policy Optimization: Uses a modified version of Proximal Policy Optimization (PPO) that can handle the complex output space of DeepSeek R1.

Example of enhanced RLHF training loop:

import torch
from models.deepseek_policy_network import DeepSeekPolicyNetwork
from models.reward_model import DeepSeekRewardModel
from rlhf.advanced_ppo import AdvancedPPOTrainer

def train_monster_behavior(initial_policy, reward_model, human_feedback_data):
    policy = DeepSeekPolicyNetwork.load(initial_policy)
    reward_model = DeepSeekRewardModel.load(reward_model)
    ppo_trainer = AdvancedPPOTrainer(policy, reward_model)

    for epoch in range(100):
        trajectories = policy.generate_complex_trajectories(num_trajectories=1000)
        human_ratings = collect_detailed_human_feedback(trajectories)
        reward_model.update(trajectories, human_ratings)
        
        ppo_trainer.train_iteration(trajectories)
        
        if epoch % 10 == 0:
            policy.save(f"deepseek_monster_policy_epoch_{epoch}.pth")

    return policy

# Usage
trained_policy = train_monster_behavior("initial_deepseek_policy.pth", "deepseek_reward_model.pth", human_feedback_data)

This enhanced RLHF system leverages DeepSeek R1's advanced language understanding and generation capabilities to create more sophisticated and contextually aware monster behaviors.