Category Archives: Artificial Intelligence

Meta-RL: given a distribution of tasks, learn a policy capable of adapting to any new task from the task distribution with as little data as possible

December 21, 2023 09:43 ,

Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson, A Survey of Meta-Reinforcement Learning, arXiv:2301.08028 [cs.LG], 2023 DOI: 10.48550/arXiv.2301.08028.

While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.

Posted in: Reinforcement learning in AI , Tagged: ,

Leveraging the unexplainability and opacity of NNs to generate random numbers

December 15, 2023 16:17 ,

Y. Almardeny, A. Benavoli, N. Boujnah and E. Naredo, A Reinforcement Learning System for Generating Instantaneous Quality Random Sequences, IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, pp. 402-415, June 2023 DOI: 10.1109/TAI.2022.3161893.

Random numbers are essential to most computer applications. Still, producing high-quality random sequences is a big challenge. Inspired by the success of artificial neural networks and reinforcement learning, we propose a novel and effective end-to-end learning system to generate pseudorandom sequences that operates under the upside-down reinforcement learning framework. It is based on manipulating the generalized information entropy metric to derive commands that instantaneously guide the agent toward the optimal random behavior. Using a wide range of evaluation tests, the proposed approach is compared against three state-of-the-art accredited pseudorandom number generators (PRNGs). The experimental results agree with our theoretical study and show that the proposed framework is a promising candidate for a wide range of applications.

Posted in: Artificial Intelligence , Tagged: ,

Limiting human intervention in the design of RL solutions (now called “Automated RL”)

December 5, 2023 10:52 ,

Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trov�, Marcello Restelli, ARLO: A framework for Automated Reinforcement Learning, Expert Systems with Applications, Volume 224, 2023 DOI: 10.1016/j.eswa.2023.119883.

Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by alleviating some of its main challenges, including data collection, algorithm selection, and hyper-parameter tuning. In this work, we propose a general and flexible framework, namely ARLO: Automated Reinforcement Learning Optimizer, to construct automated pipelines for AutoRL. Based on this, we propose a pipeline for offline and one for online RL, discussing the components, interaction, and highlighting the difference between the two settings. Furthermore, we provide a Python implementation of such pipelines, released as an open-source library. Our implementation is tested on an illustrative LQG domain and on classic MuJoCo environments, showing the ability to reach competitive performances requiring limited human intervention. We also showcase the full pipeline on a realistic dam environment, automatically performing the feature selection and the model generation tasks.

Posted in: Reinforcement learning in AI , Tagged:

Multi-task RL through common perceptions

December 5, 2023 10:50 ,

Jinling Meng, Fei Zhu, Seek for commonalities: Shared features extraction for multi-task reinforcement learning via adversarial training, Expert Systems with Applications, Volume 224, 2023 DOI: 10.1016/j.eswa.2023.119975.

Multi-task reinforcement learning is promising to alleviate the low sample efficiency and high computation cost of reinforcement learning algorithms. However, current methods mostly focus on unique features that are not conducive to the transfer between tasks. Moreover, they usually lack a balance mechanism among tasks, which often leads to the unnecessary occupation of training resources by tasks that have already been trained. To address the problems, a simple yet effective method referred to as Adaptive Experience buffer with Shared Features Multi-Task Reinforcement Learning (AESF-MTRL) is proposed. In AESF-MTRL, input observation of the environment is divided into shared features and unique features, which are extracted using different feature extractors. Unique features are extracted by simple gradient descent, while shared features are extracted using adversarial training, with an additional discriminator trained to ensure that the extracted features are indeed shared features. AESF-MTRL also maintains a reward stack to adjust the sampling ratio of trajectories from different tasks dynamically during the update period to balance the learning process of different tasks. Experiments on multiple robotics control environments demonstrate the effectiveness of the proposed method.

Posted in: Reinforcement learning in AI , Tagged: ,

Dealing with the exploration with a nice introduction to the problem

October 20, 2023 09:22 ,

Jiayi Lu, Shuai Han, Shuai L�, Meng Kang, Junwei Zhang, Sampling diversity driven exploration with state difference guidance, Expert Systems with Applications, Volume 203, 2022 DOI: 10.1016/j.eswa.2022.117418.

Exploration is one of the key issues of deep reinforcement learning, especially in the environments with sparse or deceptive rewards. Exploration based on intrinsic rewards can handle these environments. However, these methods cannot take both global interaction dynamics and local environment changes into account simultaneously. In this paper, we propose a novel intrinsic reward for off-policy learning, which not only encourages the agent to take actions not fully learned from a global perspective, but also instructs the agent to trigger remarkable changes in the environment from a local perspective. Meanwhile, we propose the double-actors\u2013double-critics framework to combine intrinsic rewards with extrinsic rewards to avoid the inappropriate combination of intrinsic and extrinsic rewards in previous methods. This framework can be applied to off-policy learning algorithms based on the actor\u2013critic method. We provide a comprehensive evaluation of our approach on the MuJoCo benchmark environments. The results demonstrate that our method can perform effective exploration in the environments with dense, deceptive and sparse rewards. Besides, we conduct sufficient ablation and quantitative analyses to intrinsic rewards. Furthermore, we also verify the superiority and rationality of our double-actors\u2013double-critics framework through comparative experiments.

Posted in: Reinforcement learning in AI , Tagged: ,

Increasing exploration when the agent performs worse, decreasing when performing better, in the context of DQN for distributing computation among cloud and edge servers, also dealing with hybridization of RL with Fuzzy

October 20, 2023 09:13 ,

Do Bao Son, Ta Huu Binh, Hiep Khac Vo, Binh Minh Nguyen, Huynh Thi Thanh Binh, Shui Yu, Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing, Engineering Applications of Artificial Intelligence, Volume 113, 2022 DOI: 10.1016/j.engappai.2022.104898.

In the age of booming information technology, human-being has witnessed the need for new paradigms with both high computational capability and low latency. A potential solution is Vehicular Edge Computing (VEC). Previous work proposed a Fuzzy Deep Q-Network in Offloading scheme (FDQO) that combines Fuzzy rules and Deep Q-Network (DQN) to improve DQN\u2019s early performance by using Fuzzy Controller (FC). However, we notice that frequent usage of FC can hinder the future growth performance of model. One way to overcome this issue is to remove Fuzzy Controller entirely. We introduced an algorithm called baseline DQN (b-DQN), represented by its two variants Static baseline DQN (Sb-DQN) and Dynamic baseline DQN (Db-DQN), to modify the exploration rate base on the average rewards of closest observations. Our findings confirm that these baseline DQN algorithms surpass traditional DQN models in terms of average Quality of Experience (QoE) in 100 time slots by about 6%, but still suffer from poor early performance (such as in the first 5 time slots). Here, we introduce baseline FDQO (b-FDQO). This algorithm has a strategy to modify the Fuzzy Logic usage instead of removing it entirely while still observing the rewards to modify the exploration rate. It brings a higher average QoE in the first 5 time slots compared to other non-fuzzy-logic algorithms by at least 55.12%, prevent the model from getting too bad result over all time slots, while having the late performance as good as that of b-DQN.

Posted in: Reinforcement learning in AI , 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: ,

A nice survey on knowledge graphs for representing, well, knowledge, focused on explainability of AI, but whatever, they are interesting for many more things

September 25, 2023 16:35 ,

Ilaria Tiddi, Stefan Schlobach, Knowledge graphs as tools for explainable machine learning: A survey, Artificial Intelligence, Volume 302, 2022 DOI: 10.1016/j.artint.2021.103627.

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions. An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations. Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research.

Posted in: Artificial Intelligence, Uncategorized , Tagged: , ,

A general model of abstraction of graphs

September 25, 2023 16:28 ,

Christer Bäckström, Peter Jonsson, A framework for analysing state-abstraction methods, Artificial Intelligence, Volume 302, 2022 DOI: 10.1016/j.artint.2021.103608.

Abstraction has been used in combinatorial search and action planning from the very beginning of AI. Many different methods and formalisms for state abstraction have been proposed in the literature, but they have been designed from various points of view and with varying purposes. Hence, these methods have been notoriously difficult to analyse and compare in a structured way. In order to improve upon this situation, we present a coherent and flexible framework for modelling abstraction (and abstraction-like) methods based on graph transformations. The usefulness of the framework is demonstrated by applying it to problems in both search and planning. We model six different abstraction methods from the planning literature and analyse their intrinsic properties. We show how to capture many search abstraction concepts (such as avoiding backtracking between levels) and how to put them into a broader context. We also use the framework to identify and investigate connections between refinement and heuristics—two concepts that have usually been considered as unrelated in the literature. This provides new insights into various topics, e.g. Valtorta’s theorem and spurious states. We finally extend the framework with composition of transformations to accommodate for abstraction hierarchies, and other multi-level concepts. We demonstrate the latter by modelling and analysing the merge-and-shrink abstraction method.

Posted in: Artificial Intelligence, Graph theory , Tagged: ,