reinforcement learning

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...

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 evolut...

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 pol...

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 searc...

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 evolut...

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...

Hybrid Reinforcement Learning “HRL” Approach with Energy Harvesting for Energy-Efficient Routing Protocols “EERP” “HRL-EERP” in Wireless Body Area Networks “WBANs” (Preprint)

BACKGROUND Wireless Body Area Networks (WBANs) revolutionize healthcare by utilizing miniature, low-power sensors, implanted or worn externally, to provide continuous, real-time tracking of vital signs like heart rate, blood pressure, temperature, and glucose levels. However, their potential is hindered by significant...

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 assig...

Asynchronous Federated Reinforcement Learning for Adaptive Resource Slicing and Low-Latency Task Offloading in Heterogeneous 6G Edge Computing Networks

AF-EdgeRL is proposed, a novel Byzantine-resilient Asynchronous Federated Reinforcement Learning framework tailored for distributed resource allocation and dynamic task offloading and establishes theoretical convergence guarantees under non-convex reinforcement learning objectives.

Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC

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...

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...