Reinforcement Learning in Traditional Industries: Revolutionizing Problem-Solving with Machine Learning

By Owain Brennan

At SeerBI, we specialize in providing data-driven solutions to businesses across maritime, logistics and traditional industries. One area of expertise that we offer is reinforcement learning. In this blog post, we will explore the concept of reinforcement learning and how it can be applied in traditional industries to optimize processes and reduce costs.

A person holding a tablet infront of machines

Reinforcement learning (RL) is a subfield of machine learning that focuses on decision-making under uncertainty. RL is modeled as an agent interacting with an environment, where the agent learns by taking actions and receiving feedback in the form of rewards or penalties. The agent’s goal is to learn the optimal policy, which is a mapping from states to actions that maximizes the cumulative reward over time.

The RL problem is usually formulated as a Markov Decision Process (MDP). An MDP is a mathematical framework that describes a decision-making problem in terms of states, actions, rewards, and transition probabilities. At each time step, the agent observes the current state of the environment, chooses an action based on its current policy, and receives a reward from the environment. The environment then transitions to a new state based on the chosen action and the transition probabilities of the MDP.

The goal of RL is to learn the optimal policy, which maximizes the expected cumulative reward over time. This is typically done using value-based or policy-based methods. Value-based methods, such as Q-learning, learn an estimate of the optimal action-value function, which maps states and actions to expected cumulative rewards. Policy-based methods, such as REINFORCE, learn a parameterized policy directly.

A diagram explaining the basics of the reinforcement learning flow

One of the main challenges in RL is data efficiency. RL agents often require a lot of data to learn optimal policies, which can be costly and time-consuming. One way to improve data efficiency is to use model-based RL, which involves learning a model of the environment dynamics and using it for planning or simulation. Model-based RL can reduce the amount of real-world interactions needed for learning, but it also introduces challenges such as model bias and complexity.

A recent paper by DeepMind1 proposes a novel model-based RL algorithm called DreamerV2 that combines several techniques to achieve state-of-the-art performance on challenging continuous control tasks. DreamerV2 uses a recurrent neural network (RNN) to learn a latent state representation of the environment that captures both deterministic and stochastic factors. It then uses an actor-critic architecture to learn a policy and a value function from imagined trajectories sampled from the RNN model. DreamerV2 also incorporates distributional reinforcement learning, which models the full distribution of returns instead of just their mean, leading to more robust estimates.

Another challenge in RL is multi-task learning. RL agents often need to adapt to different tasks or goals within a single environment or across different environments. This requires generalization and transfer skills that are not easy to acquire with standard RL methods. One way to address this challenge is to use meta-learning, which involves learning how to learn from previous experiences.

A recent paper by Facebook AI Research introduces a meta-learning framework called Meta-World that enables large-scale multi-task RL research. Meta-World consists of 50 manipulation tasks that span various difficulty levels and dimensions such as object shape, size, color, texture, etc. Meta-World provides a standardized benchmark for evaluating meta-RL algorithms on diverse and realistic tasks that require dexterity and coordination.

A third challenge in RL is real-world applicability. RL agents often face complex and dynamic environments that are not fully observable or controllable. This requires dealing with uncertainty, exploration, safety, ethics, etc., which are not well addressed by current RL methods. One way to bridge this gap is to use human feedback as an additional source of information for guiding or correcting the agent’s behavior.

ChatGPT is a smart chatbot that is launched by OpenAI in November 2022. It is based on OpenAI’s GPT-3 family of large language models and is optimized using supervised and reinforcement learning approaches.

Reinforcement learning (RL) is a technique that involves learning from trial and error by interacting with an environment and receiving rewards or penalties. ChatGPT uses RL to improve its dialogue skills by collecting human feedback online. The human feedback acts as a reward signal that guides the chatbot to generate more engaging, coherent, and appropriate responses.

ChatGPT also uses a technique called RL from Human Feedback (RLHF), which involves training an agent to maximize its expected reward based on human ratings rather than predefined metrics. This allows ChatGPT to learn from diverse and subjective preferences of different users.

This essentally means that as users are interacting with ChatGPT and accepting, rating and conversign with awsners OpenAI are collecitng furhter dat abased on human feedback to improve it’s baseline GPT models.

image of earth from space

Reinforcement Learning is also applicable and vital in industry for example in Manufacturing.

Manufacturing is a complex and dynamic industry with a high degree of variability in production processes. Reinforcement learning algorithms have been shown to be effective in improving manufacturing operations, by enabling machines to learn from experience and make decisions based on feedback.

Quality control is an important aspect of manufacturing, as defects can result in costly rework, recalls, and damage to brand reputation. Reinforcement learning algorithms can be trained to detect and correct defects in real-time, reducing the likelihood of defective products reaching consumers.

For example, RL algorithms can be used to analyze sensor data from production lines to detect anomalies that indicate a potential defect. The algorithms can then adjust the machine settings to correct the issue, minimizing the number of defective products produced.

Manufacturing processes are complex and often involve multiple machines and resources, such as raw materials and energy. Reinforcement learning algorithms can be trained to optimize these processes, reducing costs and increasing efficiency.

For example, RL algorithms can be used to optimize the cutting of materials in a production line. The algorithm can learn to minimize waste by adjusting the cutting parameters based on feedback from the environment. This results in a reduction in material waste and increased throughput, leading to significant cost savings.

Another example is energy management in manufacturing plants. RL algorithms can be trained to optimize energy usage, by learning to balance energy supply and demand across the production process. This can result in significant cost savings, as energy costs are a major expense in manufacturing.


A woman using technology in a lab

Another important sector for reinforcement learning is the Finance Sector.

Finance is a highly complex and data-intensive industry that involves making decisions based on uncertain market conditions and various forms of risk. Reinforcement learning has emerged as a powerful tool for finance professionals to analyze data and make decisions that can lead to better financial outcomes.

Portfolio management involves selecting a combination of investments that maximize the return while minimizing risk. Reinforcement learning algorithms can be used to optimize investment portfolios based on market trends and risk tolerance.

RL algorithms can learn to adjust portfolio weights in response to market movements, adjusting the risk and return profile of the portfolio. This can lead to better returns for investors and can help manage risk in volatile market conditions.

Risk assessment is a critical aspect of finance, as it involves evaluating the likelihood of financial loss. Reinforcement learning algorithms can be trained to identify and quantify various forms of risk, such as market risk, credit risk, and operational risk.

For example, RL algorithms can be used to identify patterns of fraudulent activity in financial transactions. By analyzing large amounts of transaction data, the algorithm can learn to identify anomalous behavior and alert financial institutions to potential fraud.

Fraud detection is a significant challenge for financial institutions, as fraudulent activity can result in significant financial losses. Reinforcement learning algorithms can be trained to detect and prevent fraud by analyzing large amounts of transaction data.

RL algorithms can learn to detect patterns of fraudulent activity in financial transactions, identifying anomalous behaviour and flagging potentially fraudulent transactions for further investigation. This can help financial institutions prevent fraudulent activity and protect themselves from financial loss.

At SeerBI, we believe that reinforcement learning is a powerful tool for problem-solving in various industries. By incorporating reinforcement learning into their operations, businesses can optimize their processes, reduce costs, and achieve better outcomes. Our team of data scientists has experience working with various industries to develop customized solutions using reinforcement learning. If you are interested in learning more about how reinforcement learning can benefit your business, please contact us to schedule a consultation.

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