Author Archives: Juan-antonio Fernández-madrigal

Dealing with continuous spaces in Q-learning by maintaining several spaces, each one corresponding to a particular time-step

October 13, 2023 09:45 ,

Joao Pedro Araujo, Mario A.T. Figueiredo, Miguel Ayala Botto, Control with adaptive Q-learning: A comparison for two classical control problems, Engineering Applications of Artificial Intelligence, Volume 112, 2022 DOI: 10.1016/j.engappai.2022.104797.

This paper evaluates adaptive Q-learning (AQL) and single-partition adaptive Q-learning (SPAQL), two algorithms for efficient model-free episodic reinforcement learning (RL), in two classical control problems (Pendulum and CartPole). AQL adaptively partitions the state\u2013action space of a Markov decision process (MDP), while learning the control policy, i.e., the mapping from states to actions. The main difference between AQL and SPAQL is that the latter learns time-invariant policies, where the mapping from states to actions does not depend explicitly on the time step. This paper also proposes the SPAQL with terminal state (SPAQL-TS), an improved version of SPAQL tailored for the design of regulators for control problems. The time-invariant policies are shown to result in a better performance than the time-variant ones in both problems studied. These algorithms are particularly fitted to RL problems where the action space is finite, as is the case with the CartPole problem. SPAQL-TS solves the OpenAI GymCartPole problem, while also displaying a higher sample efficiency than trust region policy optimization (TRPO), a standard RL algorithm for solving control tasks. Moreover, the policies learned by SPAQL are interpretable, while TRPO policies are typically encoded as neural networks, and therefore hard to interpret. Yielding interpretable policies while being sample-efficient are the major advantages of SPAQL. The code for the experiments is available at https://github.com/jaraujo98/SinglePartitionAdaptiveQLearning.

Posted in: Reinforcement learning theory , Tagged: , ,

Modifications of Q-learning for better learning of robot navigation

October 13, 2023 09:30 ,

Ee Soong Low, Pauline Ong, Cheng Yee Low, Rosli Omar, Modified Q-learning with distance metric and virtual target on path planning of mobile robot, Expert Systems with Applications, Volume 199, 2022, DOI: 10.1016/j.eswa.2022.117191.

Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning \u2013 a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experimental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,

A nice summary of SLAM in robotics with Lidar and Cameras

October 13, 2023 09:24 ,

Chghaf, M., Rodriguez, S. & Ouardi, A.E. Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey J Intell Robot Syst 105, 2 (2022) DOI: 10.1007/s10846-022-01582-8.

Simultaneous Localization and Mapping (SLAM) have been widely studied over the last years for autonomous vehicles. SLAM achieves its purpose by constructing a map of the unknown environment while keeping track of the location. A major challenge, which is paramount during the design of SLAM systems, lies in the efficient use of onboard sensors to perceive the environment. The most widely applied algorithms are camera-based SLAM and LiDAR-based SLAM. Recent research focuses on the fusion of camera-based and LiDAR-based frameworks that show promising results. In this paper, we present a study of commonly used sensors and the fundamental theories behind SLAM algorithms. The study then presents the hardware architectures used to process these algorithms and the performance obtained when possible. Secondly, we highlight state-of-the-art methodologies in each modality and in the multi-modal framework. A brief comparison followed by future challenges is then underlined. Additionally, we provide insights to possible fusion approaches that can increase the robustness and accuracy of modern SLAM algorithms; hence allowing the hardware-software co-design of embedded systems taking into account the algorithmic complexity and the embedded architectures and real-time constraints.

Posted in: Mobile robot SLAM , Tagged: ,

A nice summary of RL applied to robot navigation

October 13, 2023 09:20 ,

N. Khlif, N. Khraief and S. Belghith, Reinforcement Learning for Mobile Robot Navigation: An overview IEEE Information Technologies & Smart Industrial Systems (ITSIS), Paris, France, 2022, pp. 1-7 DOI: 10.1109/ITSIS56166.2022.10118362.

For several years, research shows that interest in autonomous mobile robots is increasing and it has more and more grown. Autonomous mobile robots is an object of discussion but nowadays it’s an emerging topic due to the all progress related to field like autonomous driving and UAV (drones). Integrating intelligence into robotic systems requires solving various research problems, including one of the most important problems of mobile robotic systems: navigation. Find the answers to the following three questions: What is the localisation of the robot? Where are the robot going? How can it get there? presenting the solution of mobile robot navigation problem. These questions are answered by basic navigation parts which are localization, mapping and path planning. The paper present an overview of research on autonomous mobile robot navigation. First, a quick introduction to the various features of navigation. We also discuss machine learning and reinforcement learning in mobile robotics. Furthermore, we will discuss some path planning techniques. Some future directions are also suggested.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Mixing rule-based and reinforcement learning navigation for robots

October 13, 2023 09:18 ,

Y. Zhu, Z. Wang, C. Chen and D. Dong, Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction, IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 846-857, April 2022 DOI: 10.1109/TMECH.2021.3072675.

For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this article, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Clustering time series through the moments of the corresponding regimes using fuzzy

October 13, 2023 09:02 ,

Roy Cerqueti, Pierpaolo D\u2019Urso, Livia De Giovanni, Massimiliano Giacalone, Raffaele Mattera, Weighted score-driven fuzzy clustering of time series with a financial application, Expert Systems with Applications, Volume 198, 2022 DOI: 10.1016/j.eswa.2022.116752.

Time series data are commonly clustered based on their distributional characteristics. The moments play a central role among such characteristics because of their relevant informative content. This paper aims to develop a novel approach that faces still open issues in moment-based clustering. First of all, we deal with a very general framework of time-varying moments rather than static quantities. Second, we include in the clustering model high-order moments. Third, we avoid implicit equal weighting of the considered moments by developing a clustering procedure that objectively computes the optimal weight for each moment. As a result, following a fuzzy approach, two weighted clustering models based on both unconditional and conditional moments are proposed. Since the Dynamic Conditional Score model is used to estimate both conditional and unconditional moments, the resulting framework is called weighted score-driven clustering. We apply the proposed method to financial time series as an empirical experiment.

Posted in: Probability and statistics , Tagged: ,

Shorter exploration stage in RL through the use of expert (a PID) that sets the expectation of the explored action

October 13, 2023 08:50 ,

J. Enrique Sierra-Garcia, Matilde Santos, Ravi Pandit, Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer, Engineering Applications of Artificial Intelligence, Volume 111, 2022 DOI: 10.1016/j.engappai.2022.104769.

Wind turbine (WT) pitch control is a challenging issue due to the non-linearities of the wind device and its complex dynamics, the coupling of the variables and the uncertainty of the environment. Reinforcement learning (RL) based control arises as a promising technique to address these problems. However, its applicability is still limited due to the slowness of the learning process. To help alleviate this drawback, in this work we present a hybrid RL-based control that combines a RL-based controller with a proportional\u2013integral\u2013derivative (PID) regulator, and a learning observer. The PID is beneficial during the first training episodes as the RL based control does not have any experience to learn from. The learning observer oversees the learning process by adjusting the exploration rate and the exploration window in order to reduce the oscillations during the training and improve convergence. Simulation experiments on a small real WT show how the learning significantly improves with this control architecture, speeding up the learning convergence up to 37%, and increasing the efficiency of the intelligent control strategy. The best hybrid controller reduces the error of the output power by around 41% regarding a PID regulator. Moreover, the proposed intelligent hybrid control configuration has proved more efficient than a fuzzy controller and a neuro-control strategy.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

Live-RL enhancement / reduction of unsafe situations by reducing the transition possibility of unsafe actions

October 13, 2023 08:42 ,

Serhat Duman, Hamdi Tolga Kahraman, Yusuf Sonmez, Ugur Guvenc, Mehmet Kati, Sefa Aras, A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems, Engineering Applications of Artificial Intelligence, Volume 111, 2022 DOI: 10.1016/j.engappai.2022.104763.

The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.

Posted in: Reinforcement learning in AI , Tagged: ,

Interesting summary of photovoltaic modelling

October 13, 2023 08:40 ,

Serhat Duman, Hamdi Tolga Kahraman, Yusuf Sonmez, Ugur Guvenc, Mehmet Kati, Sefa Aras, A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems, Engineering Applications of Artificial Intelligence, Volume 111, 2022 DOI: 10.1016/j.engappai.2022.104763.

The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.

Posted in: Artificial Intelligence , Tagged: ,

Variation of the Newton-Rhapson algorithm that copes with noise, with some illustrative applications such as robotics

October 6, 2023 09:05 ,

D. Fu et al. Modified Newton Integration Algorithm With Noise Tolerance Applied to Robotics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 4, pp. 2134-2144 DOI: 10.1109/TSMC.2021.3049386.

Currently, the Newton\u2013Raphson iterative algorithm has been extensively employed in the fields of basic research and engineering. However, when noise components exist in a system, its performance is largely affected. To remedy shortcomings that the conventional computing methods have encountered in a noisy workspace, a novel modified Newton integration (MNI) algorithm is proposed in this article. In addition, the steady-state error of the proposed MNI algorithm is smaller than that of the Newton\u2013Raphson algorithm under a noise-free or noisy workspace. To lay the foundations for the corresponding theoretical analyses, the proposed MNI algorithm is first converted into a homogeneous linear equation with a residual term. Then, the related theoretical analyses are carried out, which indicate that the MNI algorithm possesses noise-tolerance ability under various noisy environments. Finally, multiple computer simulations and physical experiments on robot control applications are performed to verify the feasibility and advantage of the proposed MNI algorithm.

Posted in: Mathematics , Tagged: ,