Reinforcement Learning: An Industrial Perspective

Introduction Reinforcement learning formalizes the concept of learning from interactions.1 Broadly, reinforcement learning focuses on a setting in which an agent (decision maker) sequentially interacts with an environment that is partially unknown to them. At each stage, the agent takes an action and receives a reward. The objective of the agent is to maximize rewards accumulated in the long run. There are many situations in health care where decisions are made sequentially for which reinforcement learning approaches could prove useful for decision making. Throughout this article, we consider treatment prescription as an archetypical example to connect reinforcement learning concepts to a health care setting. In this setting, the care provider, the prescribed treatment, and the patients can be viewed as the agent, the action, and the environment, respectively, as depicted in Figure 1.Figure 1: Sequential treatment of AKI or CKD complications modeled as a reinforcement learning problem.Background In this section, with the objective of making reinforcement learning literature more accessible to a clinical audience, we briefly introduce related fundamental concepts and approaches. We refer the interested reader to Sutton and Barto1 for a comprehensive introduction to reinforcement learning. Markov Decision Processes Markov decision processes (MDPs) are a formalism of the sequential decision-making problem that has been central to the theoretical and practical advancements of reinforcement learning. In each stage of an MDP, the agent observes the state of the environment and takes an action, which, in turn, results in a change of the state. This change of state is assumed to be probabilistic with the next state being determined only by the preceding state, the chosen action, and the transition probability. The agent also receives a reward that is a function of the taken action, the preceding state, and the subsequent state. In an MDP, the objective of the agent is to maximize the return defined as the reward accumulated over a time horizon. In some applications, it is common to consider the horizon to be infinite, in which case the future rewards are discounted by a factor smaller than one. The selection of action by the agent on the basis of the observed state is known as the policy. More formally, a policy is a probabilistic mapping from states to each possible action. Because the policy and the reward are a function of the state, it is critical to estimate the utility of being in a certain state. More specifically, the value function is defined as the expected return starting from a given state under the chosen policy. Under this formalism, the objective of the agent is to find the optimal policy that maximizes the value function for all states. Reinforcement Learning Methods Action-value methods are a class of reinforcement learning methods in which the actions are chosen on the basis of the estimation of their long-term value. A prominent example of an action-value method is Q-learning in which the agent iteratively takes actions with the highest estimated values and updates the action-state value function on the basis of new observations. Policy gradient methods are another class of reinforcement learning methods that seek to optimize the policy directly instead of choosing actions on the basis of their respective estimated value. Such methods could be advantageous in health care applications that entail a large number of possible actions, e.g., when recommending a wide range of drug dosages or treatment options. Clinical Applications Reinforcement learning frameworks and methods are broadly applicable to clinical settings in which decisions are made sequentially. A prominent clinical application of reinforcement learning is for treatment recommendation, which has been studied across a variety of diseases and treatments including radiation and chemotherapy for cancer, brain stimulation for epilepsy, and treatment strategies for sepsis.2–5 In such treatment recommendation settings, a policy is commonly known as a dynamic treatment regime. There are various other clinical applications of reinforcement learning including diagnosis, medical imaging, and decision support tools (see refs. 2–5 and the references therein). Reinforcement Learning in Nephrology Although there have been recent applications of machine learning in nephrology,6,7 to the best of the authors' knowledge, the application of reinforcement learning to nephrology has been primarily limited to optimizing the erythropoietin dosage in hemodialysis patients.8,9 However, there are other settings where reinforcement learning has the potential to improve patient care in nephrology. For example, reinforcement learning methods can be adopted in the treatment of the complications of AKI or CKD (Figure 1). In this problem, the state models the conditions of the patient (e.g., vital signs, laboratory test results including urine and blood tests, and urine output measurements). The action refers to the treatment options (e.g., the dosage of medications such as sodium polystyrene sulfonate, and hemodialysis). The reward models the improvement in patient conditions. Similarly, reinforcement learning can help automate and optimize the dosage of immunosuppressive drugs in kidney transplants. Challenges and Opportunities Despite the success of reinforcement learning in several simplified clinical settings, their large-scale application to patient care faces several open challenges. The complexity of human biology complicates modeling clinical decision making as a reinforcement learning problem. The state space in such settings is often enormous, which could make a purely computational approach infeasible. Moreover, modeling all potential objectives a priori as a reward function may not be feasible. To overcome these challenges and realize the potential of reinforcement learning, clinical insight can play a pivotal role. More specifically, restricting the state space to only include highly relevant clinical variables could greatly reduce the computational complexity. Furthermore, using inverse reinforcement learning,2 relevant reward functions can be learned from retrospective studies assuming the optimality of clinical decisions. Another critical challenge is addressing moral and ethical concerns. It is imperative to ensure that reinforcement learning methods do not cause harm to the patient. To this end, there exists a need for a thorough validation of such methods before their use in patient care. Hence, there is a need to go beyond retrospective studies that have been used for the proof of concept of most existing reinforcement learning methods in health care applications.2,3 The lessons learned from the success of reinforcement learning in other application areas (e.g., self-driving cars) can help navigate the path to realizing its potential in health care. Accessible open-source simulation environments that enable researchers to compare various approaches are essential to the field of reinforcement learning. OpenAI Gym is currently the leading toolkit containing a wide range of simulated environments, e.g., surgical robotics.10 The development of high-quality and reliable simulation environments for nephrology and other health care applications can facilitate the development and validation of reinforcement learning methods beyond limited retrospective studies. The adoption of methods validated in such simulation environments in actual clinical settings will require clinicians' oversight. Similar to how self-driving cars require a human driver to ensure collision avoidance, clinicians' oversight is critical to ensure the safety of the patients, especially in the early stages of the adoption of reinforcement learning methods. The data from clinicians' decisions (e.g., overruling the automated treatment recommendation) can be used to improve the reliability of autonomous systems over time and reduce the burden of clinicians' oversight.

Reinforcement Learning (RL) is a successful and established Artificial Intelligence (AI) method, particularly with recent groundbreaking progress in Deep Reinforcement Learning (DRL). Reinforcement learning is very well suited for sequential decision-making tasks, wherein a learned agent learns an optimal policy after many interactions with an environment. The present paper examines the application of reinforcement learning for automating screening of literature in academic research, particularly in the fields of computer science and e-learning. Keyword filtering techniques, while predominantly applied, are found to be inflexible as well as unable to capture the dynamic nature of research themes. To overcome such constraints, we present a Deep Q-Network (DQN)-based reinforcement learning model that combines reinforcement learning with the Semantic Scholar API to enhance research paper classification based on dynamically acquired decision rules. The proposed reinforcement learning model was trained and tested with a dataset of 8,934 research papers, accessed by systematic searching. The agent filtered and picked 11 effective papers depending on improved selection criteria like publication date, keyword relevance, and scholarly topic provided. The model iteratively optimizes the decision-making process through reward-based learning and therefore maximizes categorization accuracy over time. Test experiments demonstrate utilization of RL-based suggested framework yields classification accuracy at 91.5%, recall at 86.3%, and precision at 89.7%. A comparison test demonstrates that the approach performs 12.5% better on recall and 9.8% better on accuracy compared to traditional keyword-filtering methods. The finding confirms the power of the model in minimizing false positives and false negatives for screening literature, hence proving the scalability and adaptability of reinforcement learning in managing high academic data. This work offers a scalable, cognitive approach to conducting systematic reviews of literature through the application of reinforcement learning to programmatically execute work in academic research. The work shows the promise of reinforcement learning to further enhance research methodology, make literature reviews more effective, and facilitate more knowledgeable decision-making in fast-changing scientific disciplines. Further research will be focused on incorporating hybrid AI models with multi-agent systems of reinforcement learning for responsiveness and classification enhancements.

This paper presents a comprehensive study on the application of reinforcement learning (RL) techniques for autonomous vehicle navigation in complex urban environments. We propose a novel deep RL framework that combines state-of-the-art algorithms with realistic urban traffic simulations to train robust navigation policies. Our approach leverages a hierarchical learning structure to decompose the challenging urban driving task into more manageable sub-tasks. Extensive experiments in simulated urban scenarios demonstrate that our method significantly outperforms baseline approaches in terms of safety, efficiency, and adaptability to diverse traffic conditions. We also conduct real-world validation tests to verify the transferability of learned policies to actual autonomous vehicles. Our results highlight the potential of RL-based techniques to enable safe and efficient autonomous navigation in dynamic city environments.

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

The rapid advancement of autonomous robots in domains such as healthcare, transportation, and disaster response necessitates robust ethical decision-making frameworks to mitigate risks associated with uncertain environments. This research delves deeply into the application of reinforcement learning (RL) for enabling "safe" decisions in robots facing multiple risky outcomes. By synthesizing over 80 peer- reviewed sources, including recent 2024-2025 surveys on ethical RL and safe RL benchmarks, it systematically addresses three pivotal research questions: (1) How should a robot choose between two risky paths? (2) Can RL augmented with reward shaping effectively reduce harm in unpredictable environments? (3) How do prominent RL algorithms compare in terms of safety efficacy in robotic applications? Key findings indicate that multi-objective RL (MORL) and constrained policy optimization variants, such as Proximal Policy Optimization (PPO) with Lagrangian constraints, outperform traditional RL in balancing ethical imperatives with task performance, achieving up to 40% reductions in violation rates in simulated benchmarks. Reward shaping, particularly potential-based and ethics-informed variants, accelerates ethical convergence by 3-6x while curbing reward hacking—a common pitfall where agents exploit reward loopholes. A novel student-led simulation in a custom gridworld environment demonstrates that shaped rewards enhance average returns by approximately 12% with near-zero harm incidents over 1,000 episodes. This expanded study proposes an integrated Ethical Safe RL (ESRL) framework, incorporating human-in-the-loop feedback and verifiable constraints, tailored for accessible implementation in educational settings. It underscores persistent challenges like distributional shifts in real-world deployment and calls for interdisciplinary ethics integration. By bridging theoretical surveys with empirical simulations, this work empowers high-school researchers to contribute to responsible AI robotics.

Definition: Efficient and scalable exploration of complex problem spaces.Methods Used: Reinforcement learning (RL) and surrogate models.Problems: 1) Accelerator control for Booster at Fermi National Laboratory (FNAL), 2) Block copolymer (BCP) self-annealing control, 3) Electrical Power Grid (current work) Software: EXARL scalable RL framework and applications: 1) Neural-network-based digital twin of FNAL Booster, 2) BCP partial differential equation (PDE)-based simulations Results: Open Source EXARL scalable framework.Functioning RL applications using EXARL: 1) ExaBooster; 2) ExaCH (BCP control).EXARL testing via Bsuite and PyTest.Next Steps: Plans for collaboration on RL for fusion energy facility control.EXARL scalable RL system components and workflow.

Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

Learning scheduling control knowledge through reinforcements

This paper introduces a method of learning search control knowledge in schedule optimization problems through application of reinforcement learning. Reinforcement learning is an effective approach for the problem faced by the agent that learns its behavior through trial‐and‐error interactions with a dynamic environment. Nevertheless, reinforcement learning has a difficulty of slow convergence when applied to the problems with a large state space. The paper discusses the case‐based function approximation technique, which makes reinforcement learning applicable to the large scale problems such as a job‐shop scheduling problem. To show effectiveness of the approach, reinforcement learning is applied to acquire search control knowledge in repair‐based schedule optimization process. Preliminary experiment results show that repair‐action selection made by learned search control knowledge succeeded in improving scheduling quality efficiently.

The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications.

With the large-scale access of a high proportion of new energy sources, there is a high degree of uncertainty on both sides of the source and load, which brings huge challenges to the optimal dispatch of the power system. Therefore, accurate power prediction information of the source and load can provide important decision support for the dispatch of the new power system. In recent years, with the development of artificial intelligence technology, reinforcement learning (RL) has been gradually used in uncertain power supply power prediction, load prediction, etc., so as to support the stable and safe operation of power grid under the uncertainty of source and load. Therefore, reinforcement learning has a great application prospect in the new power system dominated by new energy. Based on this, this paper will conduct a research review on the application of reinforcement learning technology to wind power forecasting, photovoltaic power forecasting, load power forecasting, and source-load power forecasting under extreme weather. Besides, the reinforcement learning algorithm is used to predict the distributed power supply of a wind farm in Jilin Province, China and its region, which is used as the support of the calculation example. Finally, the development direction of reinforcement learning applied to source-load power prediction is prospected and analyzed.

The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment.

In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work.

Production scheduling involves the efficient allocation of limited resources to perform necessary tasks, while maintenance scheduling aims to proactively plan repair activities to avoid machine breakdowns. Traditionally, various approaches such as heuristics and mathematical programming have been employed to tackle these challenges. However, with the advent of Industry 4.0 technologies and their ability to collect real-time system data, Reinforcement Learning has emerged as a promising solution for integrated scheduling optimization problems. In this context, the present study explores the literature addressing the integration of order-dispatching and maintenance scheduling using Reinforcement Learning. The findings of this research contribute to highlight research opportunities based on the identified gaps in the literature. This paper serves as a steppingstone for future research in this area, providing a foundation for researchers to delve deeper into this integrated problem and explore innovative approaches. Approaches that consider spare parts, technician skills, product quality and different types of transportation, besides incorporating factors such as supply chain parameters, sustainability and energy efficiency, are pointed out as possible research directions in this area. As Industry 4.0 continues to evolve, the application of Reinforcement Learning in production and maintenance scheduling optimization holds great promise for improving industrial practices.

Application of Reinforcement Learning to Generate Non-linear Optimal Feedback Controller for Ship's Automatic Berthing System