Understanding Reinforcement Learning from Human Feedback (RLHF): A Practical Guide

Reinforcement Learning (RL) is a cornerstone of modern artificial intelligence, teaching machines to make decisions by trial and error. Yet, the complexity of real-world applications often makes it challenging to define clear reward functions. Reinforcement Learning from Human Feedback (RLHF) enhances traditional RL by integrating human judgment to guide the learning process. This article will explain RLHF and walk you through a realistic implementation example.

The RLHF Concept

RLHF blends traditional RL algorithms with human-derived insights. In complex tasks where reward functions are difficult to articulate, RLHF uses human feedback to steer the learning agent toward desired behaviors. This fusion of human preferences into the learning loop can result in more aligned and ethically aware AI systems.

Code Example

To illustrate RLHF, we will implement a simulated example using Python and the OpenAI Gym environment. Our goal is to teach an agent to balance a pole on a cart, incorporating human-like feedback to influence the learning process.

1. Setting Up the Environment:

Ensure you have the necessary libraries installed:

!pip install Gymnasium stable-baselines3
!pip install 'shimmy>=0.2.1'        

Import the libraries and initiate the environment:

import gym
from stable_baselines3 import PPO
new_step_api=True
env = gym.make('CartPole-v1')        

2. Defining the Human Feedback Loop:

We simulate realistic human feedback with a function that penalizes the agent for deviations from certain thresholds, emulating human preferences for the pole's angle and the cart's position.

import numpy as np

def human_feedback(observation):
    cart_position, _, pole_angle, _ = observation
    pole_angle_threshold = 0.05  # Approx 5 degrees
    cart_position_threshold = 0.5  # 0.5 units from the center
    
    pole_deviation = np.abs(pole_angle) - pole_angle_threshold
    cart_deviation = np.abs(cart_position) - cart_position_threshold
    
    feedback = 0
    if pole_deviation > 0:
        feedback -= pole_deviation
    if cart_deviation > 0:
        feedback -= cart_deviation
    
    return feedback        

3. Training with Human Feedback:

Next, we construct our model and introduce the human feedback into the reward signal during training.

from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.monitor import Monitor

def custom_reward(observation, reward):
    return reward + human_feedback(observation)

# Initialize the environment
env_id = 'CartPole-v1'
env = make_vec_env(env_id, n_envs=1, monitor_dir="/test", wrapper_class=Monitor)

# Initialize the model
model = PPO("MlpPolicy", env, verbose=1)

# Training loop
n_episodes = 5
for episode in range(n_episodes):
    obs = env.reset()
    done = False
    while not done:
        action, _states = model.predict(obs, deterministic=True)
        new_obs, rewards, dones, info = env.step(action)

        # Apply the custom reward function for each environment
        custom_rewards = [custom_reward(o, r) for o, r in zip(new_obs, rewards)]

        # Learning step
        model.learn(total_timesteps=1, reset_num_timesteps=False)

        obs = new_obs
        done = all(dones)  # Update the done condition        

4. Evaluating the Model: After training, observe the agent's performance in the environment:

for episode in range(5):
    observation = env.reset()
    done = False
    total_reward = 0
    while not done:
        action, _states = model.predict(observation)
        observation, reward, done, info = env.step(action)
        total_reward += reward
    print(f"Total reward for episode {episode}: {total_reward}")        

RLHF represents a significant stride toward developing AI that embodies human values and preferences. By following the steps in this guide, we've shown how to incorporate simulated human feedback into a reinforcement learning model. The future of RLHF holds the promise of even more nuanced and context-aware AI by deepening the integration of human feedback into the learning process.

https://colab.research.google.com/drive/1xreJdeI8lgGiFs3cRUwdlHR-ZPrWC_Fv?usp=sharing