Machine Learning – Introduction to Reinforcement Learning

Reinforcement Learning (RL) – is a more general form of machine learning than supervised learning or unsupervised learning. It learns from interaction with the environment to achieve a goal or simply learns from reward (positive adjustment) and punishments (negative adjustment).

This learning is inspired by behaviourism phycology. Point to note here is, there is nothing anthropomorphization in reinforcement learning. Unlike conventional supervised learning, which relies on predefined datasets, RL thrives in scenarios where feedback is sequential and delayed. Its iterative approach enables real-time improvement, creating smarter systems with each interaction.

This transformative capability positions RL as a cornerstone of next-generation AI, offering unprecedented potential in domains ranging from robotics to financial modeling and beyond.

Reinforcement Learning algorithms can be inherently unstable or divergent when used with neural networks due to what is known as the “deadly triad”—a combination of function approximation, bootstrapping, and off-policy learning. These components, while powerful individually, can lead to unexpected instabilities in learning if not managed properly.

Part-2 : Reinforcement Learning – Reward for Learning

Machine Learning – Introduction

Machine learning has evolved from early computational models to an essential tool shaping industries today. At its core, ML allows systems to learn from data and make predictions or decisions without being explicitly programmed. Initially, we had supervised learning, where models learn from labeled data, and unsupervised learning, which identifies patterns in unlabeled data. Over time, more advanced methods like semi-supervised and reinforcement learning (RL) emerged.

AILabPage defines machine learning as “A focal point where business, data and experience meet emerging technology and decide to work together“. ML instructs an algorithm to learn for itself by analyzing data. Algorithms here learn a mapping of input to output, detection of patterns, or reward.

The more data it processes, the smarter the algorithm gets. Thanks to statistics, machine learning became very famous in the 1990s. Machine Learning is about the use and development of fancy learning algorithms. The intersection of computer science and statistics gave birth to probabilistic approaches in AI. You can follow the link below for more details on Machine Learning.

• Machine Learning – How What and Why

In other words, Machine learning algorithms “learn” from the observations. When exposed to more observations, the algorithm improves its predictive performance. Although ML is magnificent implementing this bundle pack of ML for practical use in businesses is still a hurdle for many.

ML’s current excellence can be explained by several reasons, such as those provided below although not exclusively.

• The explosion of big data
• Hunger for new business and revenue streams in this business shrinking times
• Advancements in machine learning algorithms
• Development of extremely powerful machine with high capacity & faster computing ability
• Storage capacity

Reinforcement learning, in particular, stands out within this landscape. Unlike supervised learning, where the model learns from labeled examples, RL emphasizes learning through trial and error. Here, agents interact with an environment, receiving feedback in the form of rewards or penalties. This method mimics real-world decision-making, and during my work at AILabPage, I’ve witnessed how RL can drive systems to optimize long-term strategies and actions, making it a key player in the ML space.

What is Reinforcement Learning

Before we get into the what and why of RL, let’s find out some history of RL and how it originated. From the best research, I got the answer as it was termed in the 1980s while some research was conducted on animal behavior.

Especially how some newborn baby animals learn to stand, run, and survive in a given environment. Rewards are a means of survival from learning, and punishment can be compared with being eaten by others.

Reinforcement learning operates around a core set of concepts—agents, environments, states, actions, and rewards. In practical terms, RL is an approach where there’s no pre-defined solution or answer key, and the agent must determine its course of action to complete a task. From my hands-on experience in the AILabPage lab, it feels like training a system in a trial-and-error manner, learning from each interaction with the environment.

The agent, much like human behavior modeled on behaviorist psychology, decides on actions based on its current state, all while aiming to maximize cumulative reward over time. This dynamic is crucial, especially in complex tasks where traditional supervised learning can’t offer direct guidance.

What makes RL unique is that, unlike many other machine learning models, it doesn’t rely on pre-existing training data. Instead, the agent learns from experience as it goes. It explores the environment, taking actions, receiving feedback (rewards), and refining its strategy based on those experiences. It’s a fascinating process to witness in action—like a system learning to “think on its feet” and improving over time. At AILabPage, we’ve seen how the agent collects these training examples and iteratively improves its decision-making process.

• this action was good
• that action was bad

The learner is not told explicitly which action to take but is expected to discover which action yields the most lucrative result in the form of a reward and try the method. Typically, an RL setup is composed of two components: an agent and an environment.

Reinforcement Learning vs Supervised Learning vs Unsupervised Learning

Reinforcement learning addresses a very broad and relevant question; How can we learn to survive in our environment?

Semi-supervised learning, which is essentially a combination of supervised and unsupervised learning can also be compared with RL. It differs from reinforcement learning as it has direct mapping whereas reinforcement does not.

Reinforcement Learning – History

From the best research, I got the answer as it got termed in the 1980s while some research study was conducted on animals behaviour. Especially how some new-born baby animals learn to stand, run, and survive in the given environment. Rewards is a survival from learning and punishment can be compared with being eaten by others.

• Evolution of Learning Paradigms: Machine learning has transitioned from rule-based systems to more sophisticated models, such as deep learning, allowing machines to learn complex patterns and make decisions with minimal human intervention. This shift enables breakthroughs in fields like natural language processing and image recognition.
• Supervised vs. Unsupervised vs. Reinforcement Learning: While supervised learning is focused on learning from labeled data and unsupervised learning identifies hidden patterns, reinforcement learning stands apart as it emphasizes learning from interaction with the environment, with agents optimizing actions through rewards.
• Real-World Applications of RL: In addition to robotics and gaming, reinforcement learning is being applied in diverse areas like finance, healthcare, and autonomous systems, where models continuously improve performance through feedback loops, leading to enhanced decision-making processes over time.

In a simple term, the concept behind reinforcement learning is sort of adjustments algorithm makes. In machine learning, there is no such simulation of the human limbic system where it involved in emotion, motivation, memory or learning. The algorithm simply adjusts itself in a positive manner in case it needs reward like human brain release dopamine when you play candy crush, upon clearing the level, you get encouraged to move on even if you don’t have time or parents are shouting on your head. It just uses human terminology i.e. reward/punishment to prove that it’s inspired with the biological entity.

Demystifying – Reinforcement Learning (RL)

Reinforcement Learning (RL) algorithms learn to react to the environment. TD-learning and Q-Learning are two of the best algorithms in this learning. I remember reading a book on reinforcement learning some years back with a focus on  “Intelligent Machines”. Three methods of 3 methods of reinforced learning were discussed at a greater level as below.

• Q-Learning – This is commonly used in a model-free approach. The value update rule is the core of the Q-learning algorithm. Q-learning policies are is greedy. Q and TD are related, but not the same.  Q learning is one form of reinforcement learning in which the agent learns an evaluation function over states and actions from policy and value iteration
• Temporal Difference Learning –  TD-learning seems to be closest to how humans learn in this type of situation.
• Model-Based – best when MDP can’t be learned.

Reinforcement Learning (RL) is around for many years as the third pillar for Machine Learning. It is now becoming increasingly important for Data Scientist to know when and how to carry out. RL has some goals such as decision process, reward/penalty systems and recommendation systems.

Reinforcement Learning Algorithms

In the below picture, you will find one/two liner description for widely used RL algorithms. Please note these will be described in full chapters with calculation and examples in later posts. How all limitations will react when the same will be performed on quantum computers? It will be amazing and astonishing to see how each model will be created and parameterized.

In this article, we have overviews the major algorithms in reinforcement learning. Each algorithm will be explained in detail in upcoming posts with formula, graphics, python code and live examples. In short, it’s correct to say that reinforcement learning is all about taking actions to reap maximum rewards or face penalties if it fails. Deployment of this learning is to find the best possible path or solutions in a given problem situation

Some extra complex algorithms

• Trust Region Policy Optimization (TRPO) – It has consistent high performance but the computation and implementation this is extremely complicated.
• Proximal Policy Optimization (PPO, OpenAI version) –  PPO proposes a clipped surrogate objective function.
• BackPropagation – The reinforcement learning a kind of machine learning and uses several of its techniques including a neural network, to make the best learning model. Since it may use a neural network, the backdrop may be used in reinforcement learning.

Markov decision processes(MDP)

Reinforcement learning is closely related to dynamic programming approaches to Markov decision processes (MDP). MDP solve a partially observable problem. POMDPs received a lot of attention in the reinforcement learning community. As its a process of discrete-time stochastic control to provide a mathematical framework for decision-making modelling. A Markov Decision Process model contains:

• A set of possible world states S
• Set of possible actions A
• Real-valued reward function R(s, a)
• Description T of each action’s effects in each state.

There are so many things unexplored and with the current craze of data science and machine, learnings applied reinforcement learning, is certainly a breakthrough. Some outputs are known and some are under the control of decision-makers. MDP model contains.

How Does Reinforcement Learning Work?

Reinforcement Learning (RL) works through the interaction between an agent and its environment, where the agent makes decisions to maximize long-term rewards. The process involves learning from trial and error, where the agent observes the state of the environment, takes an action, and receives feedback in the form of rewards or penalties.

Over time, the agent refines its strategy through this continuous process, improving its decision-making abilities.

• Decision-Making Process in RL: The agent makes decisions by evaluating different actions and selecting the one that maximizes its cumulative reward, learning from past experiences and feedback from the environment.
• Exploration vs. Exploitation: The agent constantly faces the challenge of exploring new actions to discover better strategies versus exploiting the best-known action to maximize immediate rewards. This balance is critical for efficient learning.
• Dynamic Strategy Adjustment: RL agents adapt their strategies over time, learning from a changing environment and adjusting their decisions based on new insights, constantly refining their approach to improve future outcomes.

RL relies heavily on the exploration versus exploitation trade-off. Exploration is when the agent tries new actions to discover potentially better rewards, while exploitation involves choosing actions that have already yielded positive results. Balancing these two is crucial, as too much exploration can waste resources, while too much exploitation may prevent the agent from discovering better strategies.

Core Workflow

Reinforcement Learning (RL) is like teaching a curious child how to ride a bike. You don’t explicitly tell them every movement to make; instead, you let them learn by doing—falling, adjusting, and improving through trial and error.

This hands-on, experiential learning is what makes RL so exciting and intuitive when applied in practice.

1. Agent and Environment: Imagine the RL agent as the child learning to ride, and the environment as the bike, road, and weather conditions. The agent takes actions—pedaling or balancing—and observes how the environment responds, like wobbling or falling.
2. States and Actions:
   • State: This is the agent’s current situation, such as whether the bike is stable or leaning.
   • Action: Choices available to the agent, like adjusting the handlebar or speeding up.
3. Reward System: The agent learns through rewards. A small success, like balancing for a second longer, gives a positive reward, while a fall gives a penalty. Over time, the agent understands which actions lead to higher cumulative rewards.
4. Policy and Exploration:
   • Policy is the agent’s strategy, like leaning left when the bike tilts right. It evolves as the agent learns from its experiences.
   • The agent must balance exploration (trying new actions) with exploitation (choosing actions it knows work well). In the lab, tuning this balance—through techniques like epsilon-greedy—was a crucial part of experiments.
5. Learning from Experience: At AILabPage, we observed how the agent refines its decisions using methods like:
   • Q-Learning: Storing and updating a Q-table to keep track of which actions are best for each state.
   • Deep RL: Using neural networks when the state-action space becomes too large, like predicting how to balance when the road has unexpected bumps.

Hands-On Insights

In practice, creating robust RL models involves:

• Defining the Right Rewards: Small tweaks can drastically change the agent’s behavior. In one project, increasing the penalty for falling off the bike led to faster stabilization.
• Simulations Matter: Real-world environments, like varying terrains or unpredictable weather, are hard to replicate. We often used custom simulators to accelerate learning before testing in real scenarios.
• Continuous Fine-Tuning: RL isn’t just “set and forget.” Monitoring performance and iterating based on new insights is part of the process.

Inclusive Perspectives

Working in a lab taught me that RL isn’t a one-size-fits-all solution. It’s a framework for solving problems in creative, context-specific ways, whether you’re optimizing energy grids, teaching robots to walk, or even playing video games. Each application requires empathy for the domain—understanding the unique challenges and designing accordingly.

Why It’s Transformative

RL mirrors life: we’re all agents in our environments, learning through feedback loops, improving iteratively, and balancing risks with rewards. This parallel is what makes RL not just a tool for AI, but a lens for understanding decision-making and growth.

Formulating- Reinforcement Learning (RL) Problem

Reinforcement Learning – Enables an agent (Including human) to learn in an interactive environment by trial and error using feedback from its own actions and experiences. Key terms to formulate reinforcement learning as follows.

• Environment: Physical environment in which the agent (any agent) operates.
• Action (A): All the possible moves that the agent can make
• State (S): Current situation returned by the environment or State/situation in which an agent is operating currently.
• Reward (R): Immediate return sends back from the environment to evaluate the last action. Feedback from the environment on the work done.
• Policy (π): Agents strategy to determine the next action based on the current state. It’s a method to map the agent’s state to actions.
• Value (V): Expected long-term return with discount, as opposed to the short-term. The future reward that an agent would receive by taking action in a particular state/states.
• Q-value or action-value (Q): Q-value is similar to Value, except that it takes an extra parameter, the current action a.

Vπ(s) is defined as the expected long-term return of the current state “s” under policy π. Qπ(s, a) refers to the long-term return of the current state s, taking action “a” under policy π. Learning style here is more concerned with how an agent ought to take actions. It learns from interaction with the environment to meet a goal or simply learns from reward and punishments.

Challenges in Reinforcement Learning

Reinforcement Learning algorithms can be inherently unstable or divergent when used with neural networks due to what is known as the “deadly triad”—a combination of function approximation, bootstrapping, and off-policy learning. These components, while powerful individually, can lead to unexpected instabilities in learning if not managed properly.

• Function Approximation: Neural networks approximate value functions but can generalize poorly if the architecture or training process isn’t well-tuned.
• Bootstrapping: Estimating value functions based on previous estimates (like in Q-learning) introduces errors that propagate over time.
• Off-Policy Learning: Learning a target policy while exploring a different policy can lead to divergence without careful design, such as in Experience Replay.

To combat this, advanced techniques like Double Q-learning, Prioritized Experience Replay, and soft updates for target networks are applied to stabilize training in RL systems.

Types of Rewards in Reinforcement Learning

In reinforcement learning, rewards are more than just numbers—they’re the signals that shape an agent’s behavior and learning trajectory. From my hands-on exploration in the AILabPage lab, I’ve seen how the design and type of rewards directly influence the speed and quality of learning. Understanding different reward types helps tailor the agent’s training to specific goals, ensuring optimal outcomes in complex environments.

Positive and Negative Rewards

Positive rewards encourage desirable behavior by signaling success. They act as a motivator, reinforcing actions that bring the agent closer to its goal.

• Positive Rewards for Reinforcement: Positive rewards signal the agent to repeat successful actions. For example, in a game, gaining points for reaching a checkpoint directly reinforces the sequence of steps leading to success, aligning with the goal of maximizing cumulative rewards over time.
• Negative Rewards and Penalization Dynamics: Negative rewards penalize actions that result in failures or inefficiencies, like a robot incurring penalties for hitting an obstacle. These penalties push the agent to avoid such behaviors and explore alternative strategies to achieve its objectives.

The interplay between positive and negative rewards forms the foundation of reinforcement learning. While positive rewards drive goal-oriented behaviors, negative rewards act as corrective feedback, preventing suboptimal actions. Striking the right balance is a critical challenge, as excessive reliance on either can disrupt the agent’s ability to learn efficiently and generalize across tasks.

Immediate vs. Delayed Rewards

Immediate rewards provide instant feedback for an agent’s actions, making it easier for the agent to connect its choices with outcomes.

• Immediate Rewards for Simpler Tasks: Immediate rewards provide direct feedback, enabling the agent to quickly associate specific actions with their outcomes. This approach is well-suited for straightforward tasks with clear cause-effect relationships, ensuring rapid learning and response optimization.
• Delayed Rewards and Temporal Challenges: Delayed rewards require the agent to understand how current actions influence future outcomes, demanding advanced techniques like reward discounting and Q-learning. These scenarios are critical in long-term strategy problems, such as financial planning or complex games like chess, where benefits unfold over time.

Immediate rewards excel in tasks with clear and simple cause-effect dynamics, allowing rapid learning. In contrast, delayed rewards introduce temporal complexity, necessitating methods to predict long-term outcomes and bridge the gap between actions and their eventual results. My hands-on experiments revealed that addressing delayed rewards effectively often involves strategies like reward discounting or reinforcement algorithms tailored for long-term planning.

Shaping Rewards for Optimal Learning

Reward shaping is a technique to guide agents by providing additional, intermediate rewards. This strategy helps break down complex tasks into smaller, manageable milestones, allowing the agent to learn more efficiently.

• Incremental Rewards for Stepwise Progress: Incremental rewards, such as providing feedback for reaching intermediate waypoints, can accelerate learning by breaking down complex tasks into manageable steps. However, over-shaping can cause the agent to prioritize intermediate rewards over achieving the ultimate objective.
• Iterative and Thoughtful Reward Design: Crafting an effective reward structure is an iterative process that demands both technical expertise and creativity. Properly shaped rewards ensure alignment between the agent’s learned behavior and the overarching goals, enhancing efficiency and success in RL models.

Shaping rewards by breaking tasks into incremental milestones can boost learning efficiency, but care must be taken to prevent over-shaping, which risks derailing focus from the end goal. My hands-on experience highlights the importance of an iterative, thoughtful approach to reward design, ensuring agents learn effectively and their behaviors align with desired outcomes. Well-crafted rewards are the backbone of successful reinforcement learning implementations.

Mathematics Behind Rewards

In reinforcement learning, mathematics underpins the agent’s learning mechanism, particularly through reward functions and Bellman equations. Reward functions assign numerical scores to guide an agent’s actions, while Bellman equations provide a recursive framework to estimate cumulative rewards. These mathematical constructs are the foundation for designing efficient RL models, a key insight honed through practical experimentation at AILabPage.

Reward functions mathematically represent an agent’s goals, using R(s,a) to quantify the reward for taking action aaa in state sss. Bellman equations, expressed as V(s) = max⁡_a [R(s,a) + γ⋅V(s′)] , recursively estimate the value of states by combining immediate rewards with discounted future rewards. From experience, mastering these equations ensures agents balance short-term gains with long-term optimization, crucial for solving complex tasks.

Reinforcement Learning – When to use

The answer to the question above is not simple (trust me, though it could be purely my own opinion). Which kind of ML algorithm should use does not depend as much on your problem than on your dataset. Real life business use cases for Reinforcement Learning. Some major domains where RL has been applied are as follows:

There are a lot of other industries and areas where this set of skills is in use and changing the game, like computer networking, inventory management, vehicle navigation, and many more. Markov decision processes (MDP) solve the partially observable problem, and POMDPs have received a lot of attention in the reinforcement learning community. There are so many things unexplored, and with the current craze of data science and machine learning, applied reinforcement learning is certainly a breakthrough.

Conclusion – Reinforcement Learning addresses the problem of learning control strategies for autonomous agents with least or no data. RL algorithms are powerful in machine learning as collecting and labelling a large set of sample patterns cost more than data itself. Learning chess game can be a tedious task under supervised learning but RL works swiftly for the same task. The trial-and-error method as it attempts its task, with the goal of maximizing long-term reward can show better results here.

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Points to Note:

All credits if any remains on the original contributor only. We have covered reinforcement machine learning in this post, where we reward and punish algorithms for predictions and controls. Data used here are either very less or waiting for the data. Last posts on Supervised Machine Learning and Unsupervised Machine Learning got some decent feedback. Our next post will talk about Reinforcement Learning — Markov Decision Processes

Books & Other Material Referred

• Open Internet & AILabPage (group of self-taught engineers) members hands-on lab work.
• Reinforcement Machine Learning – An Introduction 

Feedback & Further Question

Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

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