reinforcement learning

Antifragile Intelligence: A Triadic Framework for AI Governance, Digital Forensics, and Sovereignty in Emerging Economies

In an era defined by extreme Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), artificial intelligence (AI) governance must transcend passive compliance checklists to become an embedded, adaptive socio-technical architecture. This paper proposes a triadic synthesis of Reinforcement Learning (RL), Generative AI (GenAI), and Cybersecurity, organized within a Seven-Layer Integrated Architecture spanning perception, cognition, adaptation, generation, protection, embodiment, and governance. Central to the framework is a formal isomorphism between Predictive Processing (PP) and Reinforcement Learning, in which both systems minimize prediction error through Bayesian updating (Friston, 2010; Friston et al., 2009). This isomorphism is operationalized through a safety-constrained objective function that treats variational free energy as a regularizer, mitigating the class of failures known as “reward hacking” (Laidlaw et al., 2025; Shihab et al., 2025; Skalse et al., 2022). Illustrative comparison of the Asynchronous Advantage Actor-Critic (A3C) algorithm against legacy Q-Learning suggests materially faster and more stable policy convergence under the resource-constrained, high-packet-loss conditions typical of emerging economies. By integrating the sub-Saharan African relational philosophy of Ubuntu/Unhu with global AI4People principles (Floridi et al., 2018; Van Norren, 2023; Yilma, 2025), the framework embeds explicit digital forensics workflows and blockchain-anchored chain-of-custody protocols (Atlam et al., 2024; Patil et al., 2024). The framework is further extended and empirically grounded through a twentyproject, four-cluster Edge-AI case portfolio spanning domestic safety, environmental intelligence, sustainable energy and agriculture, and healthcare accessibility in the Indian context, demonstrating the triadic architecture’s applicability from enterprise-scale governance to grassroots micro, small, and medium enterprise (MSME) innovation. This synthesis serves as a blueprint for organizations in the Southern African Development Community (SADC) and India to assert digital sovereignty, ensuring that autonomous systems are antifragile, context-sensitive, and designed for communal flourishing rather than extractive optimization.

Data for "Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies"

Data for "Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies" Raw seed-level data and campaign manifests for a benchmark of five agents (Random, Adaptive Heuristic, Q-Learning, DQN, Double DQN) on EV charging-station assignment, simulated on real Rabat and Tangier (Morocco) road networks in SUMO. Includes: campaign manifests with SHA-256 provenance, raw per-seed CSVs for the confirmatory trained/untrained diagnostic (three scenarios) and the legacy 210-run benchmark, and the JSON summaries behind the manuscript's result tables. Integrity verifiable via the included SHA-256 manifest. Preliminary, data-only deposit. Simulation event logs and trained model weights are not included in this version; available from the corresponding author on request.

Intelligent agents: architectures, learning paradigms, deployment models, and applications

The Cognitive Familiarity Supremacy Theory (CFST)

The Cognitive Familiarity Supremacy Theory (CFST) proposes that a substantial portion of human certainty, ideological attachment, collective identity formation, and perceived superiority emerges not primarily from objective rational evaluation, but from repeated familiarity encoding mechanisms operating within subconscious cognitive architectures.This framework argues that repeated environmental exposure, social conditioning, emotional reinforcement, identity fusion, symbolic repetition, and institutional amplification collectively construct familiarity-driven epistemic structures that are frequently mistaken for objective truth, rational certainty, or universal superiority. The theory synthesizes and mathematically formalizes principles from cognitive neuroscience, psychology, sociology, political theory, philosophy of mind, epistemology, systems theory, information theory, complexity science, behavioral economics, evolutionary biology, communication studies, artificial intelligence, anthropology, cybernetics, and cultural theory into a unified explanatory framework.CFST introduces a comprehensive causal chain model:Repeated Exposure \rightarrow Subconscious Encoding \rightarrow Identity Fusion \rightarrow Emotional Reinforcement \rightarrow Bias Formation \rightarrow Perceived Superiority.The theory proposes that human cognition operates through familiarity-weighted interpretive systems, where the subjective sensation of certainty often emerges from accumulated familiarity intensity rather than objective verification.The framework further integrates: Bayesian epistemology, predictive processing, Hebbian learning, social identity theory, information entropy, network propagation, algorithmic amplification, memetic evolution, cultural conditioning, political hegemony, and neurocognitive attractor-state dynamics.The theory also develops: formal mathematical models, belief topology equations, dynamic systems formulations, stochastic familiarity propagation systems, agent-based ideological simulations, network-theoretical belief diffusion structures, and computational cognitive equilibrium equations.At the civilizational level, CFST proposes that societies are partially constructed upon collectively reinforced familiarity architectures rather than purely objective truth systems. At the individual level, it explains ideological rigidity, nationalism, fanaticism, cultural supremacy perception, identity-protective cognition, and epistemic polarization.Finally, the theory proposes that genuine epistemic liberation requires conscious disruption of subconscious familiarity monopolies through critical reasoning, diversity exposure, meta-cognitive awareness, and reflective epistemological reconstruction.

Reinforcement Learning in Wearable Robotic Systems for Orthopedic Rehabilitation: An Elbow-Focused Narrative Review

Orthopedic rehabilitation after upper-limb trauma increasingly emphasizes protected early motion, quantitative monitoring, and patient-specific assistance. This narrative review examines how reinforcement learning (RL) may contribute to wearable robotic systems for orthopedic rehabilitation, with emphasis on elbow-centered applications and transferable evidence from upper-limb exoskeletons, prosthetic-control studies, and musculoskeletal simulation. Literature from clinical and engineering sources was synthesized across four themes: clinical rationale, device platforms, control architecture, and translational readiness. The reviewed evidence suggests that the most plausible near-term platform is an externally worn powered orthosis rather than an implanted robotic joint. Across studies, RL is most defensible as a supervisory or personalization layer that adapts assistance within hard constraints on torque, speed, and range of motion, rather than as an unconstrained end-to-end controller. Multimodal sensing, especially combinations of electromyography, kinematics, and interaction sensing, appears more robust than any single intent channel. Digital twins and musculoskeletal simulators provide a practical substrate for offline training and conservative policy transfer, but fracture-specific clinical validation remains limited. Key barriers include alignment, comfort, safety governance, and the persistent gap between simulation and bedside deployment. Overall, the literature supports a staged translational strategy centered on hierarchical control, conservative safety design, and clinically bounded personalization.

Primary Field Cybernetics

Primary Field Cybernetics: From Spectral-Phase Flow to a Symformic Theory of Control This article develops Primary Field Cybernetics (PFC) as a cybernetic theory derived from the ontology of Symformism and the field architecture of Dynamical Informational Field Theory (DIFT). Symformism treats an enduring form not as a static object but as a relational organization that preserves its identity through continuous change, exchange, perturbation, and reorganization. DIFT provides a physical research architecture for this intuition through the complex spectral-phase Primary Organizational Field, organized phase current, structural memory, adaptive access geometry, mobility, impedance, and dynamostatic persistence. From these relations, the article derives a domain-neutral cybernetic architecture: Primary Field → organized flow → retained history → differential impedance → differential accessibility → viable future action → control. The central proposition is that control cannot be reduced to selecting an action from a fixed repertoire. A system’s history can alter the practical accessibility of its future responses. Consequently, an enduring actant participates in reorganizing the conditions under which its own later regulation remains possible. On this basis, PFC distinguishes state control, access control, reflexive control, and relational metacontrol, and introduces the concept of accessible variety: regulatory variety understood not merely as nominally available responses, but as responses that remain practically reachable within relevant constraints of cost, delay, compatibility, and viability. The paper situates this proposal in relation to classical and second-order cybernetics, including Maxwell, Wiener, Ashby, Beer, von Foerster, Maturana and Varela, and gives particular attention to Marian Mazur’s theory of autonomous systems. It also distinguishes the proposed architecture from active inference, allostasis, reinforcement learning, eligibility traces, synaptic plasticity, and meta-learning. A deliberately limited numerical model is included as an illustration of one consequence of the theory rather than as validation of PFC or DIFT. It examines whether different histories can produce different future response accessibility under an otherwise matched challenge. Causal ablation and a single-timescale trace equivalence control are used to delimit what this example does and does not establish. The article concludes with a falsification programme based on matched-state, divergent-history experiments, in which present observables are matched, accessibility is measured before a decisive response, identical perturbations are applied, and the proposed access mechanism is selectively manipulated. Candidate applications include physical, biological, neural, artificial, and institutional systems. The resulting formulation shifts the fundamental cybernetic question from “How does a system correct its present state?” toward “How does an enduring organization preserve and reorganize the conditions under which viable future action remains accessible?” Keywords: Primary Field Cybernetics; Symformism; DIFT; Primary Organizational Field; spectral-phase flow; phase current; dynamostasis; impedance; access geometry; accessible variety; cybernetics; control; memory; resilience.

A Twin Delayed Deep Deterministic-based control method for a full vehicle semi-active suspension system

Through this system, users can input parameters for a vehicle semi-active suspension using a magneto-rheological damper (sprung mass, sprung mass centroid position parameters, unsprung mass, magneto-rheological damper model parameters, suspension spring stiffness, wheel equivalent spring stiffness, balance bar torsion spring stiffness, and ground excitation). Through this program's calculations, a vehicle suspension deep reinforcement learning controller model can be obtained to optimize the shock absorption effect at the vehicle's center of mass. Development hardware environment: CPU Intel Core i5 11600k, Memory: 32GB, Hard disk space: 4TB; Runtime hardware environment: CPU Intel Core i5 10400, Memory: 8GB, Hard disk space: 500GB. Development software environment: Windows 10; Runtime software environment: Windows 10.

Key Developments in Reinforcement Learning in Robotics and Practical Implications: A Comprehensive Survey

This narrative review examines the current state of knowledge regarding Reinforcement Learning in Robotics within the broader context of Artificial Intelligence. We survey the theoretical foundations, methodological approaches, and key findings that have shaped the field, identifying major themes and tracing the evolution of ideas over time. The review synthesizes evidence from multiple research traditions and highlights both established conclusions and areas of ongoing debate. Particular attention is given to recent advances that have opened new avenues for investigation and to the practical implications of theoretical developments. We conclude with a discussion of the most promising directions for future research, emphasizing the importance of interdisciplinary collaboration and methodological innovation.

AgentCreditBench: A Conformance-Test Suite for Turn-Level Credit Estimators

AgentCreditBench is a CPU-first conformance-test suite for turn-level credit assignment in agentic reinforcement learning. It compares GRPO, RLOO, GAE, GiGPO, Monte Carlo, and custom estimator outputs with exact policy advantages on tiny finite-horizon Markov decision processes, and separately evaluates the induced policy-gradient signal.

Worlds Without Handcrafted Limits: A Narrative Review of Procedural Content Generation from Perlin Noise to Machine Learning

Procedural content generation (PCG)---the algorithmic creation of game levels, terrain, quests, and rules---has evolved from a memory-saving trick into one of game development's most active research areas. This article presents a narrative review of the field's canonical line: Perlin's 1985 image synthesizer, the search-based taxonomy of Togelius and colleagues, the ACM survey of Hendrikx and colleagues, the Springer volume of Shaker, Togelius, and Nelson, the AI-and-games synthesis of Yannakakis and Togelius, the machine-learning turn of Summerville and colleagues, and the reinforcement-learning frontier of Khalifa and colleagues. The synthesis is organized around three themes: foundations, in which noise functions, grammars, and search established the generative toolbox; design, in which PCG met authorship---level design as search space, evolution as game designer, mixed-initiative tools; and learning, in which generative models trained on human-authored corpora opened PCGML and its controllability problem. It is concluded that PCG's history is the progressive relocation of authorship---from the asset to the generator---and that controllability is the field's central open problem.

Key Developments in Reinforcement Learning in Robotics and Practical Implications: A Comprehensive Survey

This narrative review examines the current state of knowledge regarding Reinforcement Learning in Robotics within the broader context of Artificial Intelligence. We survey the theoretical foundations, methodological approaches, and key findings that have shaped the field, identifying major themes and tracing the evolution of ideas over time. The review synthesizes evidence from multiple research traditions and highlights both established conclusions and areas of ongoing debate. Particular attention is given to recent advances that have opened new avenues for investigation and to the practical implications of theoretical developments. We conclude with a discussion of the most promising directions for future research, emphasizing the importance of interdisciplinary collaboration and methodological innovation.

Learning by Consequence: A Narrative Review of Reinforcement Learning from Thorndike's Law of Effect to Deep Q-Networks and AlphaGo

Reinforcement learning---learning what to do from reward and punishment rather than from instruction---unifies animal psychology, optimal control, and machine learning into one computational program, and its deep-learning era delivered the field's most visible artificial intelligence achievements. This article presents a narrative review of the canonical line: Thorndike's 1911 law of effect, Bellman's 1957 dynamic programming, Samuel's 1959 checkers player, Sutton's 1988 temporal-difference learning, Watkins and Dayan's 1992 Q-learning, Tesauro's 1995 TD-Gammon, Sutton and Barto's 1998 synthesis, Mnih and colleagues' 2015 Deep Q-Network, Silver and colleagues' 2016 AlphaGo and 2017 AlphaGo Zero, Lillicrap and colleagues' continuous control with DDPG, and Schulman and colleagues' 2017 proximal policy optimization. The synthesis is organized around three themes: foundations, in which the credit-assignment problem received formal solutions in value functions and temporal difference; scaling, in which function approximation, experience replay, and self-play converted tabular theory into high-dimensional control; and algorithmic consolidation, in which actor-critic methods and policy gradients stabilized practice. It is concluded that reinforcement learning's contribution is a general grammar of goal-directed learning---and that its open problems, sample efficiency and reward specification, define the frontier between artificial and natural intelligence.