Reinforcement learning (RL) is a branch of machine learning that trains agents to make decisions by interacting with their environment. It stands alongside supervised and unsupervised learning as one of the three main pillars of artificial intelligence. (www.aigence.io/post/machine-learning-unlocking-business-innovation-and-growth)
As succinctly put by Sutton and Barto, authors of Reinforcement Learning: An Introduction:
"The most important feature distinguishing reinforcement learning from other types of learning is that it uses training information that evaluates the actions taken rather than instructs by giving correct actions. This creates the need for active exploration—an explicit search for good behaviour. Purely evaluative feedback indicates how good the action taken was, but not whether it was the best or worst action possible."
In simpler terms, RL is about learning through exploration rather than relying on pre-labelled datasets. Agents actively experiment in their environment, learning through feedback loops. This process mirrors how humans and animals learn, which has made RL a significant area of cross-disciplinary research, blending ideas from psychology and neuroscience.
For example, temporal difference learning—a key RL algorithm—has been applied in behavioural neuroscience to understand dopamine-based decision-making.
How Reinforcement Learning Works
At its core, reinforcement learning relies on Markov Decision Processes (MDPs), which model the interaction between an agent and its environment in a series of discrete steps. This framework enables RL to optimise actions over time. The key components of an RL system include:
- Policy: The "brain" of the agent, defining how it selects actions based on its current state. Policies can be deterministic (specific actions are always taken) or stochastic (actions are chosen probabilistically).
- Reward Signal: A numerical value representing the goal of the agent. After every action, the agent receives feedback in the form of a reward.
- Value Function: A long-term perspective on what is "good" for the agent. It predicts the cumulative reward an agent can expect from a given state or action, helping the agent prioritise future rewards over immediate gains.
- Model of the Environment: Some RL systems use a model to simulate outcomes and guide decision-making. In model-based RL, this simulation enables more efficient learning. In contrast, model-free RL relies solely on trial-and-error, which can be less efficient but more adaptable to complex environments.
Historical Foundations of Reinforcement Learning
RL has evolved from three key areas of research, which eventually converged to form the modern field:
- Optimal Control and Dynamic Programming: Originating in engineering and mathematics, this field tackled problems involving dynamic systems and introduced concepts like value functions and Markov decision processes. Early research focused on designing controllers, but it laid the groundwork for RL by formalising how decisions could be optimised.
- Trial-and-Error Learning: Rooted in psychology, this thread explored how animals learn through reinforcement. Pavlov’s classical conditioning and Thorndike’s law of effect highlighted how behaviours change based on prior experiences. This area saw a revival with Harry Klopf’s work, which argued that supervised learning missed out on the benefits of biological reinforcement.
- Behavioural Neuroscience: Temporal difference learning, inspired by neuroscience, studies how agents can improve their strategies by comparing predicted rewards with actual outcomes. This concept has greatly influenced RL algorithms.
These threads merged in the 1980s with breakthroughs like the Actor-Critic architecture by Sutton and Barto, andQ-learning, developed by Chris Watkins. Today, RL is a vibrant field with numerous algorithms and architectures tailored to various applications.
Applications of Reinforcement Learning
Reinforcement learning has found applications in diverse domains, including:
- Robotics: RL powers autonomous drones, self-driving cars, and other robots that must navigate dynamic, real-world environments.
- Finance: RL algorithms optimise trading strategies, portfolio management, and risk assessment.
- Supply Chains: RL improves inventory management, logistics, and operational efficiency.
- Healthcare: From personalised treatment plans to drug discovery, RL helps tackle complex decision-making challenges.
A notable example is RL’s role in training deep neural networks. For instance, feedback mechanisms like thumbs-up/down buttons in applications like ChatGPT allow RL to fine-tune models based on user preferences. Supervised learning lays the foundation for the model, but RL refines it to produce more effective responses by optimising for user satisfaction.
The Future of Reinforcement Learning
Reinforcement learning is poised to play a pivotal role in developing Artificial General Intelligence (AGI) and other advanced AI systems. Richard Sutton has emphasised RL’s ability to continually adapt and improve, making it crucial for solving open-ended, real-world problems. This adaptability is likely to be a cornerstone of any AGI system, allowing it to navigate unfamiliar situations and learn autonomously.
As RL continues to evolve, it will remain at the heart of cutting-edge AI technologies, enabling systems like large language models (LLMs) to refine their capabilities and deliver increasingly sophisticated interactions. The combination of RL and other machine learning techniques will drive innovation, ensuring that AI becomes more flexible, responsive, and effective.