Introduction to Reinforcement Learning

This type of learning system is called an “Agent”

AI Generated Image using ChatGPT

Imagine teaching a dog new tricks. If we reward the dog with treats when it does something right, like sitting or catching a ball. Over time, the dog learns what actions earn rewards and starts doing those more often. This way of learning by trial and error, with rewards and feedback, is the basic idea behind Reinforcement Learning (RL) in artificial intelligence.

What is Reinforcement Learning?

Reinforcement Learning is a type of machine learning where a system called an agent learns to make decisions by interacting with its environment and receiving rewards or penalties for its actions.

Instead of being told exactly what to do, the agent tries different actions and learns from the results. Good actions get rewarded, and bad actions get no reward or penalties. Over time, the agent figures out the best actions to take to maximize its rewards.

AI Generated Image using ChatGPT

Key Terms

1. Agent: The learner or decision-maker. Think of it as the “student” trying to figure out the best moves.
2. Environment: Everything the agent interacts with. It could be a game, a robot’s surroundings, or even a stock market.
3. Actions: The choices the agent can make. For example, moving left or right, buying or selling, jumping or staying still.
4. Reward: The feedback the agent gets after taking an action. A positive reward encourages the agent to repeat the action, while a negative reward or no reward discourages it.
5. State: The current situation or condition the agent is in, like the position of a game character or the market status.

How Does Reinforcement Learning Work?

1. The agent observes the state of its environment.
2. It takes an action based on what it has learned so far.
3. The environment responds with a new state and a reward.
4. The agent updates its understanding to choose better actions next time.

This cycle repeats many times, helping the agent learn the best strategies (policy) through experience.

Let’s check another example, training an agent to choose between fire and water. First, the agent observes its environment and selects an action using some basic rules. If it moves toward the fire, we deduct 50 points, and when it goes toward the water, we can reward it with 100 points. Then, based on the reward and penalty, the agent learns and updates its rules, called a policy, so it can earn more rewards next time. Similarly, many robots use reinforcement learning algorithms to learn how to walk by figuring out for themselves the best strategy through trial and error.

DeepMind’s AlphaGo program is one of the most famous success stories of Reinforcement Learning and made the headlines in May 2017, when it beat the world’s top Go player Ke Jie. The AlphaGo program learned its winning policy by checking millions of Go games and then playing many games with itself. During the game against Ke Jie, AlphaGo’s learning was turned off and it just used the policy it had learned while analyzing the multiple games. This is also called offline learning.

Another famous example of this type of learning system is Self-Driving Cars. They learns to drive safely by reacting to traffic, obstacles, and road conditions.

So, this kind of learning helps build smart systems.