Reinforcement Learning: What is it and how is it used

Reinforcement learning (RL) is one of the most exciting areas of artificial intelligence today. It’s all about teaching machines to learn from their own experiences, much like humans do. Instead of following a set of pre-defined rules, RL agents figure out how to act by trial and error — testing different actions, learning from the results, and gradually improving their decisions. This learning-by-doing approach is transforming industries from robotics to finance, making machines smarter, more adaptable, and capable of tackling complex problems that were once thought to be beyond their reach.

Unlike other AI methods that need labeled data or predefined instructions, reinforcement learning relies on continuous interaction with the environment. The agent makes decisions, sees what happens, and gets feedback in the form of rewards or penalties. Over time, it uses this feedback to learn which actions lead to the best results. The ultimate goal is to maximize long-term rewards, developing strategies that can handle real-world challenges with remarkable efficiency. RL is particularly powerful because it mimics the way living beings learn: by doing, observing outcomes, and adjusting behavior accordingly.

Key Concepts

Reinforcement learning is built on three key concepts: the agent, the environment, and rewards. Together, these elements create a feedback loop that drives learning.

• Agent: The agent is the learner or decision-maker, like a robot, a virtual player in a game, or an algorithm trading stocks. The agent’s task is to determine the best actions to take to achieve its goals.
• Environment: The environment is the space where the agent operates — anything from a video game world to a real-world scenario like driving a car, managing a warehouse, or navigating a complex social interaction. The environment responds to the agent’s actions and provides feedback, shaping the agent’s future decisions.
• Rewards: Rewards are the feedback signals that tell the agent how well it’s doing. A positive reward encourages good behavior, while a negative reward discourages mistakes. The agent’s job is to figure out which actions will maximize these rewards over time, continually adjusting its strategy to get the best results.

The learning process involves a constant balance between exploration (trying new actions to discover what works) and exploitation (using known strategies that provide high rewards). Finding this balance is crucial because too much exploration can lead to wasted effort, while too much exploitation can cause the agent to miss out on potentially better strategies. It’s like a constant game of trial and adjustment, where the agent is always refining its approach.

Real World Applications

Reinforcement learning is already making a big impact across various fields, driving innovation and creating smarter, more efficient systems:

• Robotics: RL helps robots learn tasks through practice, such as picking up objects, assembling parts, or navigating complex environments. This approach allows robots to adapt to new situations without being explicitly programmed for every scenario, making them more versatile and capable in real-world settings.
• Gaming: RL agents have mastered complex games, often beating human champions. These agents learn by playing millions of games, refining their strategies, and adapting to different opponents, showcasing RL’s ability to handle strategic planning and decision-making at a superhuman level.
• Finance: In finance, RL algorithms are used to optimize trading strategies, manage portfolios, and predict market trends. These agents learn to make fast, data-driven decisions, adapting to market changes in real-time. By continually refining their approach, they can identify opportunities and mitigate risks more effectively than traditional methods.
• Healthcare: In healthcare, RL is being used to develop personalized treatment plans, optimize clinical decision-making, and improve patient outcomes. For example, RL can help in adjusting medication dosages in real-time based on a patient’s response, making treatments more effective and tailored to individual needs.
• Language Models: Reinforcement learning can be key to improving large language models (LLMs) like OpenAI’s ChatGPT. These models learn not just from data but also from real-world interactions with users. For example, when you click “helpful” or “not helpful” after reading a response, you’re providing valuable feedback that helps the model understand what works and what doesn’t. Over time, this feedback fine-tunes the model, allowing it to adapt and improve.

Technical Applications

CartPole:

This example shows a basic agent interacting with the CartPole environment from the OpenAI Gym library. The agent takes random actions — pushing the cart left or right — and observes the resulting states, rewards, and whether the episode ends. The goal is to balance a pole on top of a moving cart, and the agent receives a reward for each time step the pole remains balanced. Once the pole goes past the angle shown below, the environment resets.

Although the agent doesn’t actually learn or improve (since it’s taking random actions), this setup introduces the key reinforcement learning framework: the agent interacts with the environment, takes actions, and receives feedback in the form of rewards. It sets the stage for understanding how an agent could learn from these interactions if a learning algorithm were applied. This example can be turned into a more advanced approach to reinforcement learning by implementing Deep Q-Learning, a reinforcement learning algorithm in the CartPole environment. Unlike the random agent, this agent learns to balance the pole by associating states with actions that maximize long-term rewards. It uses a neural network to estimate Q-values — measures of how good each action is in a given state. As the agent interacts with the environment, it updates these values based on the rewards received, gradually improving its decision-making. The agent starts by exploring actions randomly (exploration) and gradually focuses on actions that have proven successful (exploitation). This process demonstrates the core of reinforcement learning: learning from experience to make increasingly better decisions over time. The results of the model can be seen below:

Here, the epsilon value is essentially a value that represents the tradeoff between exploration and exploitation, known as an epsilon-greedy policy. More specifically, it is a percentage that controls how often you explore new, random actions compared to sticking with the best-known actions. For instance, if epsilon is 0.1, you explore new options 10% of the time and use the best-known option 90% of the time. In this example, we can see that as the number of episodes (training cycles) increases, the epsilon value decreases. This essentially means that over time, the model transitions from exploring new strategies to exploiting the best strategies that it has already found.

Grid World:

This example applies the Q-Learning algorithm to a grid world environment. The environment is a 5x5 grid where the agent learns to navigate from a starting position to a goal while avoiding obstacles. The agent moves up, down, left, or right, in order to reach the goal. Through repeated interactions, the agent learns the best actions to take in each state, gradually discovering the optimal path to the goal. The agent once again uses an epsilon-greedy policy to balance exploration] and exploitation. The results can be seen below:

We can once again see that the epsilon value decreases over time, showing that the model transitions from discovering new strategies to applying the strategies that it has already found. This example illustrates how reinforcement learning algorithms enable an agent to learn effective strategies through trial and error, optimizing its behavior to achieve the highest reward.

Conclusion

Reinforcement learning is a groundbreaking approach in AI, allowing agents to learn and adapt by interacting with their environment and learning from feedback. Unlike other AI methods, RL focuses on trial and error, helping agents improve their decisions over time.

Whether it’s balancing a pole, navigating a grid, or optimizing complex strategies, RL showcases its potential to tackle real-world challenges through continuous learning and adaptation. As RL technology advances, it promises to revolutionize industries, creating smarter, more autonomous systems that can learn, adapt, and excel in dynamic environments.