In the exciting field of robotics, the pursuit of creating robots that can perform tasks with human-like skill is paramount. One way to achieve this is by designing robots that can autonomously acquire new skills, just like humans do. However, acquiring new motor skills is a multifaceted process, involving various forms of technical learning such as supervised learning, direct programming, and imitation. Among these methods, reinforcement learning has emerged as highly potent and effective. So, what is reinforcement learning? How does it work? What are its various applications and limitations? This guide will provide you with everything you need to know about reinforcement learning.
What is reinforcement learning?
Reinforcement learning (also known as deep reinforcement learning) is a Machine Learning (ML) subfield, which focuses on teaching machines how to make decisions and take actions in an environment. It is like training computers how to learn from its own experiences, similar to how human learns through trial-and-error method.
Reinforcement learning encompasses an agent such as a robot, an environment, and the feedback or reward. The agent (robot) interacts with its surrounding environment, performs an action, and receives feedback in the form of rewards or punishments based on its actions. It aims to learn the best sequence of actions that will lead to the highest cumulative reward over time.
Let’s make this concept clearer through an example!
Imagine you have a dog, and you reward your dog every time they raise their paw on command. Or, you have a child and when your child aces a spelling test, you shower them with praise (or maybe a small reward). In both these cases, the dog and child learn what actions are considered good based on the positive responses they receive. The same theory is applied in reinforcement learning, where the agent (robot) learns from positive feedback and strives to repeat actions that result in rewards.
How does reinforcement learning work?
To understand how reinforcement learning works, let's consider a scenario of training a robotic arm to pick up objects. Here, the robotic arm is the agent, and the environment consists of objects placed on a table. The steps are as follows:
- Initialisation: Initially, the agent (robotic arm) has no knowledge about the environment or how to interact with it. It starts by taking random actions.
- Observation and action: The agent observes the current state of the environment, such as the position of the objects and the arm. Based on this observation, the agent takes an action, like moving its joints to reach for an object.
- Reward: After taking an action, the agent receives feedback from the environment in the form of a reward signal. If it successfully picks up an object, it might receive a positive reward. If it fails or knocks over other objects, it might receive a negative reward.
- Learning and optimisation: The agent uses the reward signal to update its policy. It learns to associate certain actions with higher rewards and adjusts its behaviour accordingly. Through repeated iterations, the agent improves its decision-making process and gradually learns the best actions to take in different situations.
- Exploration and exploitation: To discover new strategies, the agent balances exploration and exploitation. It explores new actions to see if they lead to higher rewards while also exploiting the actions that have proven successful in the past.
- Convergence: Over time, with continuous learning and optimisation, the agent converges on an optimal policy. It becomes proficient at picking up objects, maximising rewards, and minimising errors.
This is how the method of reinforcement learning works and enables the robotic arm to adapt and improve its performance in picking up objects without explicit programming.
What are the types of reinforcement learning?
In the world of artificial intelligence, reinforcement learning can be broadly classified into two types: positive reinforcement and negative reinforcement. In both the cases, the aim is to shape the behaviour of the agent to achieve specific goals. Let us delve deeper!
- Positive reinforcement: Positive reinforcement is characterised by an event that occurs as a result of a specific behaviour to enhance the strength and frequency of that behaviour. In simpler terms, it has a beneficial impact on behaviour. For instance, consider training a virtual dog to fetch a ball. Whenever the dog successfully retrieves the ball, it receives a treat as a reward. This positive reinforcement encourages the dog to continue fetching the ball.
- Negative reinforcement: Negative reinforcement involves removing or avoiding an unpleasant stimulus when the agent takes a desired action. The idea is to increase the possibility of the agent repeating that action in order to avoid the negative consequence. For example, a self-driving car needs to learn how to stay within the lane. Whenever the car turns off the lane, it receives a loud alarm sound. By adjusting its steering and staying within the lane, the car can avoid the unpleasant alarm, which serves as a negative reinforcement.
How is reinforcement learning different from supervised and unsupervised learning?
Reinforcement learning, supervised learning, and unsupervised learning are all vital approaches in machine learning, each functioning in unique ways. Let's delve into them using an analogy.
Imagine you're a student, and the computer is your teacher. In supervised learning, the teacher generously provides you with numerous examples and correct answers. Your task is to grasp a general rule and figure out the answers on your own. It's like having a teacher who shares all the right answers, helping you develop problem-solving skills.
On the other hand, unsupervised learning is like solving a puzzle without the teacher's guidance. You explore the available information, searching for patterns and making intriguing discoveries. There's no definitive right or wrong answer; it's all about unravelling the pieces and finding hidden connections.
Now, reinforcement learning takes a different approach. There's no teacher to provide answers, and you don't need prior knowledge of the correct solutions. Instead, you learn by trial and error, experimenting and observing the outcomes. It's like playing a game where you earn rewards for making good decisions. The more you play, the better equipped you become to win the game. Let the learning adventure begin!
What are the real-life applications of reinforcement learning?
Reinforcement learning has paved the way for numerous applications across various sectors. Here are some real-life applications where reinforcement learning has made a significant impact:
- Traffic light control system: Reinforcement Learning is used to optimise traffic signal timings. With this approach, traffic signals learn to adapt their timing based on the traffic flow in real-time, thereby reducing traffic congestion and improving overall efficiency.
- Recommendation systems: Online platforms like Netflix and YouTube utilise reinforcement learning in their recommendation systems. The system learns from user interactions and continually adjusts its recommendations, aiming to maximise user engagement over time.
- Energy management: Reinforcement learning is applied in managing energy consumption in residential and commercial settings. For instance, Google's DeepMind used reinforcement learning to reduce the energy used for cooling their data centres by 40%.
- Game playing: Perhaps one of the most famous applications of reinforcement learning is in game playing. From the simple game of noughts and crosses to complex strategy games like Go, reinforcement learning algorithms have been used to train AI models that can outperform human players.
Final thoughts
Reinforcement learning paves the way for an exciting future in the field of artificial intelligence. From optimising traffic signals to revolutionising game playing, it has staggering potential to transform our world. As we continue to unravel its capabilities, one can only imagine the advancements that lie ahead. With that said, let's embrace this technology and explore its endless possibilities.
What's next? Dive into the world of Natural Language Processing (NLP) The science behind chatbots and voice assistants. Explore another groundbreaking advancement in AI and machine learning.