Search Results (12)

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

Transnational higher education programs in engineering face persistent challenges in sustaining student motivation, engagement, and learning outcomes. Cultural norms, linguistic barriers, and traditional pedagogies often reinforce teacher-centred instruction, limiting active participation. This mixed-methods action research investigates how problem-based learning (PBL) supported by interactive handouts affects students’ motivation, engagement, and perceived learning outcomes in civil engineering programs, delivered in a Sino–UK university context. Drawing upon socio-cultural constructivism, Self-Determination Theory (SDT), and the multidimensional framework of student engagement, the study repositions motivation and engagement as central drivers of learning. Quantitative data from student surveys (N = 49) and qualitative responses from open-ended questions were analysed to identify patterns of perceived improvement and underlying mechanisms. Findings reveal that the scaffolded PBL and interactive tasks enhanced students’ intrinsic motivation, collaborative engagement, and self-reported understanding of key concepts. Students described the activities as “more interesting,” “interactive,” and “helpful for exam preparation.” In total, 92% agreed that the handouts improved their understanding of core concepts, while 78% of students reported being more motivated to participate in class, and 92% of students expressed that the handouts enhanced the learning environment. While self-reported perceptions limit causal claims, the findings contribute to a growing body of evidence advocating for learner-centred, motivationally informed pedagogies in transnational engineering education. Full article

Serious games that integrate educational content with engaging gameplay mechanics hold promise for reducing cognitive load and increasing student motivation in STEM and health science education. This preliminary study presents the development and evaluation of the Medimon NASA Demo, a game-based learning prototype designed to teach undergraduate students about the musculoskeletal and visual systems—two critical domains in space medicine. Participants (n = 23) engaged with the game over a two-week self-regulated learning period. The game employed mnemonic-based characters, visual storytelling, and turn-based battle mechanics to reinforce medical concepts. Quantitative results demonstrated significant learning gains, with posttest scores increasing by an average of 23% and a normalized change of c = 0.4. Engagement levels were high across multiple dimensions of situational interest, and 74% of participants preferred the game over traditional formats. Qualitative analysis of open-ended responses revealed themes related to intrinsic appeal, perceived learning efficacy, interaction design, and cognitive resource management. While the game had minimal impact on short-term STEM career interest, its educational potential was clearly supported. These findings suggest that mnemonic-driven serious games like Medimon can effectively enhance engagement and learning in health science education, especially when aligned with real-world contexts such as space medicine. Full article

Determining the electrochemical, thermal, and mass transfer dynamics embedded in an alkaline electrolysis (AEL) system provides important information about the application of ancillary services provided by hydrogen energy for the elimination of carbon emissions. Therefore, there is an urgent need to develop methodologies for evaluating key parameters, such as overvoltage coefficients, stack transfer capacity, diaphragm thickness, and permeability, to accurately capture the system’s fluctuating characteristics. However, limited by the lack of superior sensor technology, some significant variables cannot be measured directly. In this context, comprehensively accurate parameters of an estimation strategy offer a novel alternative to characterize the system’s corresponding intrinsic nature. This paper was motivated by this arduous challenge and aims to address the large branching factors with irregular properties. Specifically, the associated mathematical models reflecting the transient operating parameters in terms of electrochemical, heat transfer, and mass transfer are first established. Subsequently, k-means clustering analysis is conducted to deduce the similarity of distribution of the measured variables, which can function as proxies of the separator to distinguish the working status. Furthermore, online reinforcement learning (RL), renowned for its ability to operate without extensive predefined datasets, is employed to conduct dynamic parameter estimation, thereby approximating the robust nonlinear and stochastic behaviors within AEL components. Finally, the experimental results verify that the proposed model achieves significant improvements in estimation errors compared to existing parameter estimation methods (such as EKF and UKF). The enhancements are 76.7%, 54.96%, 51.84%, and 31% in terms of RMSE, NRMSE, PCC, and MPE, respectively. Full article

This study examines the intrinsic motivation of preservice primary teachers in a science education course designed with player-type personalization in gamification strategies. Using a mixed-methods approach, a one-group post-test-only design was combined with qualitative analysis. Game elements were personalized based on the HEXAD user typologies, aligning with Self-Determination Theory to support autonomy, competence, and relatedness. Quantitative data from the Intrinsic Motivation Inventory revealed high median scores across these psychological needs, suggesting that customization fostered deeper engagement. Key elements included cooperative challenges, branching narratives, and flexible participation pathways. Qualitative findings reinforced these results, highlighting students’ increased sense of agency, social connection, and investment in learning. The structured integration of narrative played a crucial role in contextualizing academic tasks, transforming the learning process into an immersive experience. Overall, the findings indicate that well-designed, personalized gamification strategies effectively bolster preservice teachers’ intrinsic motivation in this science education course. By demonstrating how player-type personalization optimizes motivation in gamified teacher education, this study contributes to the growing body of research on tailored gamification. Full article

Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function H is then designed to help judge the goodness of the emotions. Based on H, an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations. Full article

The reinforcement learning (RL) research area is very active, with an important number of new contributions, especially considering the emergent field of deep RL (DRL). However, a number of scientific and technical challenges still need to be resolved, among which we acknowledge the ability to abstract actions or the difficulty to explore the environment in sparse-reward settings which can be addressed by intrinsic motivation (IM). We propose to survey these research works through a new taxonomy based on information theory: we computationally revisit the notions of surprise, novelty, and skill-learning. This allows us to identify advantages and disadvantages of methods and exhibit current outlooks of research. Our analysis suggests that novelty and surprise can assist the building of a hierarchy of transferable skills which abstracts dynamics and makes the exploration process more robust. Full article

Reading, as one of the four basic language skills, activates language learning. Tertiary-level students often undermine this opportunity and rarely read anything in addition to their course assignments. Rapid technological developments offer additional possibilities in this domain. The present study aims to define to what extent the specifically designed web-based reading platform (Readvise) can support and improve students’ second language reading skills with the intent to transform them into self-regulated reading (SRR) skills. The focus of this design-based research is 39 undergraduate students who study English as a second language (L2). According to the results, through the elimination of the main barriers and uncertainties declared by the students when reading independently in L2, the platform contributes to the advancement of L2 reading skills of the students, encourages changes in their L2 reading behaviour, fosters metacognitive abilities, and reinforces intrinsic reading motivation. When supported consistently through the platform, these features can ensure the development and enhancement of SRR skills in the long run, contributing equally to the improvement of the students’ L2 proficiency level. Full article

Exploration in unknown dynamic environments is a challenging problem in an AI system, and current techniques tend to produce irrational exploratory behaviours and fail in obstacle avoidance. To this end, we present a three-tiered hierarchical and modular spatial exploration model that combines the intrinsic motivation integrated deep reinforcement learning (DRL) and rule-based real-time obstacle avoidance approach. We address the spatial exploration problem in two levels on the whole. On the higher level, a DRL based global module learns to determine a distant but easily reachable target that maximizes the current exploration progress. On the lower level, another two-level hierarchical movement controller is used to produce locally smooth and safe movements between targets based on the information of known areas and free space assumption. Experimental results on diverse and challenging 2D dynamic maps show that the proposed model achieves almost 90% coverage and generates smoother trajectories compared with a state-of-the-art IM based DRL and some other heuristic methods on the basis of avoiding obstacles in real time. Full article

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field. Full article

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment. Combining both goal-babbling with imitation learning, and active learning with transfer of knowledge based on intrinsic motivation, our algorithm self-organises its learning process. It chooses at any given time a task to focus on; and what, how, when and from whom to transfer knowledge. We show with a simulation and a real industrial robot arm, in cross-task and cross-learner transfer settings, that task composition is key to tackle highly complex tasks. Task decomposition is also efficiently transferred across different embodied learners and by active imitation, where the robot requests just a small amount of demonstrations and the adequate type of information. The robot learns and exploits task dependencies so as to learn tasks of every complexity. Full article

Hierarchical skill learning is an important research direction in human intelligence. However, many real-world problems have sparse rewards and a long time horizon, which typically pose challenges in hierarchical skill learning and lead to the poor performance of naive exploration. In this work, we propose an algorithmic framework called surprise-based hierarchical exploration for model and skill learning (Surprise-HEL). The framework leverages the surprise-based intrinsic motivation for improving the efficiency of sampling and driving exploration. It also combines the surprise-based intrinsic motivation and the hierarchical exploration to speed up the model learning and skill learning. Moreover, the framework incorporates the reward independent incremental learning rules and the technique of alternating model learning and policy update to handle the changing intrinsic rewards and the changing models. These works enable the framework to implement the incremental and developmental learning of models and hierarchical skills. We tested Surprise-HEL on a common benchmark domain: Household Robot Pickup and Place. The evaluation results show that the Surprise-HEL framework can significantly improve the agent’s efficiency in model and skill learning in a typical complex domain. Full article