What is Reinforcement Learning? Mechanisms and Use Cases of AI Learning through Trial and Error

1. What is Reinforcement Learning?

Reinforcement Learning (RL) is one of the methods by which AI learns through "trial and error." It is similar to how humans learn which strategies work while playing a game.

For example, let's consider a robot that moves through a maze to reach the goal.

•The robot does not know which path to take (it starts with no knowledge).
•It moves randomly to try (trial and error).
•If it moves in the right direction, it receives a "reward."
•If it hits a dead end, there is a "penalty."
•Through repeated attempts, it learns the "optimal route to reach the goal."

In other words, it is a mechanism where AI learns the optimal behavior by "rewarding good actions and penalizing bad actions."

Derivative of Reinforcement Learning: RLHF (Reinforcement Learning from Human Feedback)
RLHF (Reinforcement Learning from Human Feedback) is a method that enhances reinforcement learning by utilizing human feedback. In traditional reinforcement learning, the goal is to maximize rewards, but this approach is effective when designing those rewards is challenging or when it is necessary to teach ethically appropriate behavior. A representative application example is LLMs like ChatGPT. For instance, by providing human feedback, LLMs are trained to make the generated text feel more natural.

Fundamental Elements of Reinforcement Learning

Reinforcement learning has three key elements.

① Agent (AI and robots)
Learning entity.
Examples: game AI, robots, autonomous driving systems, etc.

② Environment (World)
The place where the agent acts.
Examples: game board, maze, driving simulation, etc.

③Rewards
Feedback obtained as a result of actions.
Example: +1 point for progressing through the maze, -1 point for hitting a wall, etc.

Agents learn to maximize rewards while acting within the environment.

Familiar Examples of Reinforcement Learning

The concept of reinforcement learning applies to various situations in everyday life.

Children Learning to Ride a Bicycle is Also 'Reinforcement Learning'
• At first, they lose balance and fall (failure).
• When they manage to go forward, they feel happy and say, 'I did it!' (reward).
• After trying many times, they become able to ride well (learning).

Dog Training with Reinforcement Learning
• Give a treat when they sit (reward).
• Get scolded when they misbehave (penalty).
• As a result, they learn that "sitting leads to good things" (learning).

2. Mechanisms of Reinforcement Learning and Main Algorithms

There are various algorithms in reinforcement learning, which are used depending on the application.

●Q-Learning
Q-Learning is a method that assigns a value called "Q-value" to each action and selects the action with the highest Q-value. It is suitable for finding optimal strategies in simple environments.

●Deep Reinforcement Learning (DQN, Deep Q-Network)
In traditional Q-learning, as the number of states (combinations of situations that the agent must consider) increases, computation becomes difficult. Therefore, DQN, which utilizes neural networks to learn optimal actions from large amounts of data, has emerged. DQN, developed by Google DeepMind, has been able to play classic Atari* games with scores exceeding those of humans.
*Atari: A U.S. company founded in 1972, primarily manufacturing video games.

●Policy Gradient
In Q-learning, we learn "how good each action is (Q-value)", but in Policy Gradient, we directly learn "how to act (Policy)".
For example, let's consider the case of moving a robotic arm.
・In Q-learning, we evaluate options like "move right" or "move left" and choose the optimal one.
・In Policy Gradient, we directly learn the flow of movements, such as "smoothly moving to the right".
Thus, Policy Gradient is well-suited for learning continuous actions, making it particularly effective in scenarios that require precise movements, such as autonomous driving and robot control.

Tasks Difficult for Reinforcement Learning

As mentioned above, reinforcement learning is not suitable for static data (tasks that are sufficient for supervised learning) because it is based on "trial and error and rewards in an environment." Tasks like image classification are efficient with supervised learning using neural networks (e.g., CNN). It is important to choose the appropriate learning method according to the objective.

3. Example of Reinforcement Learning

Reinforcement learning is a method in which AI (agents) try actions within an environment and learn better actions based on the rewards obtained as a result. Research and development, as well as practical applications, are progressing in a wide range of fields. Below are some representative examples.

●Robot Control
In industrial and household robots, reinforcement learning is utilized to enable robots to recognize their environment while learning optimal actions. For example, autonomous mobile robots can not only avoid obstacles but also learn to pick up items and navigate routes, thereby improving work efficiency. Reinforcement learning particularly supports robots to operate more flexibly and effectively in scenes where adaptability is required in dynamic and changing environments.
Reference link: Developing AI that controls complex robot operations with high precision using "offline reinforcement learning" with a small amount of data, the world's first

●Game AI
Google DeepMind's AlphaGo demonstrated the power of reinforcement learning by defeating the world champion in Go. AlphaGo refined its strategies through repeated matches and gradually began to employ advanced tactics. Game AI utilizes this reinforcement learning to predict player actions, discover new strategies and optimal moves through repeated competitions, and become a challenging opponent for players. Furthermore, game AI can learn the behavioral patterns of its opponents and adopt more human-like or optimal tactics.
Reference link: [Artificial Intelligence Challenging the Brain 18] Why Did Go AI Defeat Professional Players 10 Years Earlier?

● Autonomous Driving
In autonomous driving technology, by utilizing reinforcement learning, vehicles learn to select optimal routes and avoid other vehicles, pedestrians, and obstacles. Autonomous vehicles can recognize road conditions and surrounding environments in real-time, allowing them to make optimal decisions for safer and more efficient travel. Through reinforcement learning, vehicles learn optimal behavior patterns in various scenarios, maintaining stable performance even during long drives.
Reference link: Advanced Decision-Making with Deep Reinforcement Learning Achieves 'Level 3' on Public Roads

●LLM Model Utilizing RLHF
Conversational LLM
LLMs trained using conventional reinforcement learning learn to generate optimal responses. However, merely maximizing rewards can lead to responses that are unnatural for humans. By utilizing RLHF, the model can learn while humans evaluate whether the responses are "appropriate," allowing it to generate more natural and beneficial answers.

Content Generation (Text Summarization, Translation, etc.)
When AI summarizes news or performs translations, it not only "reduces the word count" but also adjusts to ensure that it is "readable and retains important information" by incorporating human evaluation. This allows for the generation of summaries that are natural and easy to understand for readers, rather than mechanical summaries.

Ethical Control
For example, RLHF is utilized to ensure that chatbots do not make inappropriate statements. By penalizing responses deemed inappropriate by humans and reinforcing ethically sound answers, we can build reliable AI.

4. Challenges and Future Prospects of Reinforcement Learning

Reinforcement learning is a very powerful learning method, but it has several challenges. If these challenges can be overcome, reinforcement learning will be utilized in even broader fields. Below, we detail the current challenges and their prospects.

• High Cost of Learning
Reinforcement learning is a method that learns optimal strategies through trial and error, and this process consumes a large amount of computational resources. In particular, simulations that mimic physical environments and robot control require vast computational resources and long training times, resulting in high costs. To address this issue, the development of more efficient algorithms and the establishment of computational infrastructure that can accelerate reinforcement learning are necessary.

•Time-consuming trial and error
In reinforcement learning, agents often take a long time to learn due to repeated failures, resulting in poor data efficiency and lengthy training times. This is especially true in complex environments, where it can take an enormous amount of time to discover the optimal actions. To address this issue, research is being conducted on methods that utilize simulation environments to efficiently conduct experiments in the real world, as well as new algorithms that can learn from limited data (for example, transfer learning and model-based reinforcement learning).

•Challenges of Application in the Real World
Reinforcement learning performs well in simulation environments, but there are many unpredictable factors when applying it to the real world. For example, in systems like robot control and autonomous driving, there are complexities in reality that cannot be accounted for in simulations, such as sensor accuracy, obstacle prediction, and road conditions. In the future, it is expected that technology development will progress to create more realistic environments for practical application of reinforcement learning in the real world, allowing agents to continue learning autonomously.

•Safety and Ethical Issues
Reinforcement learning agents are designed to maximize rewards, which poses a risk of unintended behaviors. This means that AI does not consider human values or ethics, but rather learns to take actions that "most efficiently achieve the goal."
For example, in the case of self-driving cars, if a reward is set to "protect the passengers in the vehicle" to avoid accidents, the AI may learn to prioritize the safety of passengers over pedestrians. However, is that behavior socially acceptable?
In the future, regulations and ethical guidelines will be crucial to ensure safety in systems utilizing reinforcement learning.

The future prospects include improving the efficiency of reinforcement learning, applying it to the real world, and ensuring safety as the main challenges. By addressing these issues, it is expected that reinforcement learning will be used more widely in autonomous systems and advanced decision support systems.

5. Summary

Reinforcement learning is a powerful method for AI to learn optimal actions through trial and error, solving complex real-world problems. It has achieved results in many fields, particularly in game AI, robot control, and autonomous driving. The evolution brought about by reinforcement learning holds the potential to make our lives more efficient, safe, and advanced.

Looking ahead, the development of efficient learning algorithms for reinforcement learning, the application of simulation environments to the real world, and the evolution of technology with consideration for ethical aspects are required. Additionally, for certain tasks, learning methods such as supervised learning may be more suitable. It is also important to choose the appropriate learning method according to the objective.

With the advancement of reinforcement learning, it is expected that in the future, an era will come where AI will autonomously and collaboratively solve problems with humans. Focusing on the development of this technology and preparing to leverage its results will be key to the future utilization of AI technology.

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