Getting Started with Reinforcement Learning: A Beginner’s Journey

4 min readSep 2, 2023

Topics Covered

- What is Reinforcement Learning?
- Why do we need Reinforcement Learning?
- Applications of Reinforcement Learning
- Important terms for Reinforcement Learning
- Beginner-friendly example to understand how it works
- Summary

What is Reinforcement Learning ?

Reinforcement learning is a type of machine learning. It works on a reward and punishment mechanism. The agent is rewarded for taking actions that lead to desired outcomes, and punish for taking actions that lead to undesired outcomes.

The agent learns from its actions in real time and improves itself by not doing wrong actions again. It learns to make better choices to get more rewards and avoid punishments.

Why do we need Reinforcement Learning?

One major drawback of traditional machine learning algorithms is their high dependency on extensive data. However, there are situations where the required data may not be available, nonexistent, or doesn’t align well with the model’s requirements. In such cases, we need a technique that can learn from its own actions and continuously improve by collecting data from those actions. This is where Reinforcement Learning comes into play.

Reinforcement learning is a type of machine learning that can learn from experience without requiring a lot of data. The agent learns by interacting with the environment and receiving feedback from the environment. The feedback can be positive (reward) or negative (punishment). The model uses this feedback to improve there actions.

Applications of Reinforcement Learning:

Reinforcement learning is a powerful tool that can be used to solve a variety of problems. Some of the most common applications of reinforcement learning include:

Game playing
Robotics
Finance
Natural language processing
Traffic control
Medical diagnosis
Supply chain management

Important terms for Reinforcement Learning

Agent: An entity that interacts with the environment and takes actions.
Environment: The system in which the agent lives and operates. The environment provides feedback to the agent based on its actions.
State: The description of the environment at a particular time. It contains all the information that the agent needs to make decisions.
Action: A possible thing that the agent can do in a given state.
Policy: A function that maps states to actions. It tells the agent which action to take in a given state.
Reward: A reward is a signal given to the agent by the environment to indicate that an action was good. Rewards can be positive or negative. Positive rewards encourage the agent to take the same action again, while negative rewards discourage the agent from taking the same action again.
Markov decision process (MDP): A mathematical model of a reinforcement learning problem.

Beginner-friendly example to understand how it works:

Imagine you have an AI-powered chess-playing robot(Chess Bot) and you want to train it to become a grandmaster-level chess player. Here’s how reinforcement learning could be applied

Initial State: ChessBot begins with little chess knowledge. It understands the basic rules but lacks strategies.

Positive and Negative Reinforcements:

Positive Reinforcement (Rewards): Whenever ChessBot wins a chess match or executes a brilliant move, you get rewards.

Negative Reinforcement (Penalties): If ChessBot makes blunders or loses a game then it didn’t get reward and get punished.

Trial and Error: ChessBot starts playing chess games. At first, it makes random moves and sometimes blunders. But when it occasionally makes a strong move or wins a game, it receives a reward.

Learning and Optimization: Over time, ChessBot begins to understand better chess strategies. It recognizes patterns and learns to predict opponents’ moves. It quickly realizes that winning games leads to reward and avoid to get punish.

Becoming a Chess Grandmaster: As ChessBot continues to play and learn from its games, it becomes a formidable chess player. It can anticipate its opponent’s moves, plan intricate strategies, and execute brilliant combinations. It regularly wins matches and enjoys the rewards.

In this example:

ChessBot is the “agent” learning through interactions with the environment.

Chess-Board is an Environment

Initial random moves represent exploration and trial and error.

ChessBot’s improved chess-playing skills demonstrate learning and optimization over time.

This scenario illustrates how reinforcement learning can help an AI-powered robot improve its performance in complex tasks like playing chess, ultimately striving to reach the level of a grandmaster player.

AlphaZero is a chess AI that was trained using reinforcement learning. It defeated the world’s best chess engines in 2017.

Summary

Reinforcement learning is a type of machine learning that allows an agent to learn from its own experiences. The agent is rewarded for taking actions that lead to desired outcomes, and punished for taking actions that lead to undesired outcomes. The agent learns from its actions in real time and improves itself by not doing wrong actions again. It learns to make better choices to get more rewards and avoid punishments.

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