Understanding the Basics of Reinforcement Learning

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Reinforcement Learning is the area of AI focused on building systems that learn from experience or trial and error. This post uncovers the basic concepts and applications of this intriguing part of AI in a nontechnical and approachable way.

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What is Reinforcement Learning?

Reinforcement Learning (RL) is a branch of AI where an agent — typically a software program — gradually learns to make decisions intelligently through interaction with its environment.

To better understand the rationale behind RL, a common comparison is that of a young kid learning to ride a bicycle. At first, the kid initially tries out different actions, often leading to a fall. Every time the kid falls off the bike, (s)he experiences pain (punishment), whereas if the kid manages to ride a few meters without falling, (s)he feels satisfied (reward). The kid gradually internalizes which actions -or sequences of actions- lead to smooth riding, applying them and improving their riding skills.

Similarly, in RL an agent performs actions that lead to rewards or punishments and iteratively adjusts its behavior to improve its performance over time.

Elements of an RL Algorithm

 
The first step to understanding the basics of RL is introducing the key elements of an RL algorithm. These elements are illustrated in the diagram below.

• Agent: a software entity that makes decisions and takes actions in an environment to achieve a goal.
• Environment: The digital or physical setting the agent interacts with, by executing actions.
• State: The environment is composed of states, such that the agent is in a certain state at a given time. In other words, a state represents the current environment situation, being “analyzed” by the agent to make decisions.
• Action: Any move or decision made by the agent in a given state, normally leading to a new state as a result of the action.
• Reward: A value or feedback received by the agent as a result of taking an action leading to a new state. It can be positive or negative (punishment), indicating an immediate success or failure of the action made concerning the objective defined. Positive rewards tend to bring the agent closer to such an objective, and vice versa.

How Does an RL Agent Learn?

Our next question to answer is: how does the agent learn to choose actions that lead to maximum rewards, both in the short and the long term? Put another way, which elements does the agent utilize during the learning process to improve its decision-making capabilities over actions at different states? This is where the concepts of policy and value function come into the scene.

A policy is the strategy used by the agent to decide which action to take at every possible state. In the simplest case, the policy can be a states-actions lookup table, but normally it is defined by a more complex mathematical function that maps states to possible actions. For example, an agent who learns to play a platform-based video game whose controlled character is currently standing on a platform (state) can walk forward, backward, or jump in either direction (actions).

Meanwhile, the reward function quantifies the positive or negative reward the agent receives upon executing an action at a given state. Mathematically, it maps a state-action pair to a numerical reward. In the video game example, if the character is on an edge and jumps forward, it may reach another platform opposite him (positive reward), whereas if it decides to walk forward without jumping, it will fall (negative reward).

When observed jointly, these elements allow the agent to gradually learn the best courses of action leading to maximizing a reward and eventually achieving the goal pursued.

Model-based vs Model-free RL Approaches

Where do the policies and reward functions come from? Does someone formulate this information about environment states, actions, and their rewards? The short answer is: it depends. This information is gathered differently depending on the type of RL approach used. There are two broad approaches under this perspective: model-based RL and model-free RL.

Model-based RL uses a model of the environment (often learned upon data via machine learning or deep learning) to assess the outcomes of actions taken, whereas model-free RL gradually constructs this model through direct environment interaction, relying on pure “trial and error” rather than predictions or estimates.

Real-World Impact and Hurdles in RL

Applications and Latest Trends

At an experimental level, RL has historically been applied to solving games and problems in simulated environments. Still, its application areas have rapidly expanded to areas like robotics and recommender engines, where real-time decisions in dynamic environments are required. Latest application trends of RL include autonomous vehicle control, and its integration with Generative AI (for instance, language models), to improve content generation decisions in complex or highly changeable settings.

Challenges and Limitations

One of the major challenges of RL is its high computational and data consumption cost, due to intensive interactions with the environment to learn effectively, thereby making its application in certain real-world scenarios more difficult.

Wrap-Up

 
In this article, we walked through the basic concepts surrounding RL algorithms and their central component: agents with a goal that perform actions by interacting with an environment and gradually learn from the result of these actions. We also outlined the salient real applications and challenges of implementing RL solutions. While challenging, RL is currently experiencing a lot of popularity due to its symbiotic relationship with Gen-AI solutions, making them much more effective at content generation.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.