Definition
Reinforcement Learning (RL) is a machine learning approach where an agent learns optimal behaviours by interacting with an environment through trial and error. The agent receives rewards or penalties based on its actions, gradually developing strategies to maximise cumulative rewards. RL is used in complex decision-making scenarios like robotics, game playing, autonomous systems, and optimisation problems, with algorithms like Q-learning and policy gradient methods enabling adaptive learning.
Reinforcement learning, often shortened to RL, is a type of machine learning where a system learns how to make decisions by interacting with its environment. Instead of being told exactly what to do, it figures things out for itself through experience.
At the centre of RL is something called an autonomous agent. This is any system that can make decisions and act without direct human instructions at every step. Robots and self driving cars are good examples. They sense what is happening around them and choose what to do next.
Learning by trial and error
In reinforcement learning, the agent is not given labelled examples of right and wrong answers. Instead, it learns through trial and error. It tries actions, sees what happens, and receives feedback in the form of rewards or penalties from its environment.
Over time, the agent learns which actions lead to better outcomes and starts to prefer those. This makes RL especially useful for situations where decisions happen in a sequence and the environment is uncertain, like driving in traffic or moving through a busy warehouse.
How RL differs from other machine learning
RL is often compared with supervised and unsupervised learning. However, there are important differences between those machine learning techniques:
- Supervised learning uses labelled data, where the correct answers are already known
- Unsupervised learning looks for hidden patterns in unlabelled data
- Reinforcement learning does not rely on labels or pattern finding alone. It learns by acting, receiving rewards, and improving its behaviour over time
- Another key difference is that RL treats data as connected steps in a sequence, not as independent records. Each decision affects what happens next.
The basic loop: agent, environment, reward
Reinforcement learning is usually described as a loop between three things: the agent, the environment, and a goal.
The agent observes the current state of the environment. A state is simply a description of what is going on at that moment. The agent then chooses an action. The environment responds by moving to a new state and giving a reward signal. If the reward is positive, the action is encouraged. If not, the agent learns to avoid it.
This process, often framed as a Markov decision process, repeats again and again. Gradually, the agent builds up experience about which actions work best in which situations.
Exploration versus exploitation
A big challenge in RL is the balance between exploration and exploitation.
- Exploration means trying new actions to discover what might work better
- Exploitation means using actions that have worked well before
If the agent only explores, it never settles on good behaviour. If it only exploits, it might miss better options. Successful RL systems constantly balance both.
Key building blocks of RL
Several important ideas shape how RL systems work:
- Policy: The rule or method the agent uses to decide what action to take in each state
- Reward signal: The feedback from the environment that defines the goal, such as fewer collisions or shorter travel time for a vehicle
- Value function: An estimate of how good a state is in the long term, not just the immediate reward
- Model: An optional part that predicts how the environment might respond to actions, helping the agent plan ahead
Online and offline learning
RL agents can learn in two main ways.
- Online learning: The agent learns by directly interacting with the environment in real time
- Offline learning: The agent learns from previously collected data without interacting with the environment during training. Offline approaches are useful when real world interaction is risky, expensive, or difficult.
Different ways to do RL
Researchers have developed many methods for reinforcement learning. Some focus on learning the value of actions, while others focus more directly on learning the policy itself.
- Dynamic programming breaks problems into smaller steps and uses a model of the environment
- Monte Carlo methods learn purely from experience by looking at complete sequences of actions and rewards
- Temporal difference learning updates its learning step by step, combining ideas from both
A well known example of a value based method is Q learning. Q learning teaches the agent to estimate how good each action is in each state, in terms of future rewards. By repeatedly updating these estimates, the agent learns to choose actions that lead to the highest long term benefit.
Another major family is policy gradient methods. Instead of mainly learning action values, these methods directly adjust the policy so that actions leading to higher rewards become more likely. They are especially useful in complex or high dimensional problems where it is hard to compare every possible action using value estimates alone.
There are also hybrid approaches, such as actor critic methods, which use both a policy and a value estimate to guide learning.
Where RL is used
Reinforcement learning is especially suited to complex decision making in changing environments.
In robotics, RL helps machines learn how to move and perform tasks in the real world. In language technology, it can improve how chatbots and other systems make sequences of decisions in dialogue. In general, RL shows promise wherever systems must act over time and learn from the consequences.
Key takeaways
- Reinforcement learning is a way for machines to learn decisions through trial and error
- An autonomous agent interacts with an environment and learns from rewards and penalties
- RL differs from supervised and unsupervised learning because it focuses on actions and long term outcomes
- The agent must balance exploring new actions with exploiting known good ones
- Core ideas include policy, reward signal, value function, and sometimes a model of the environment
- Q learning is a common value based method, while policy gradient methods learn the decision policy directly
- RL is useful in robotics, intelligent vehicles, and systems that must make sequences of decisions in uncertain settings