Results 151 to 160 of about 6,930,474 (290)
Imitating Cost-Constrained Behaviors in Reinforcement Learning
Proceedings of the International Conference on Automated Planning and SchedulingComplex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems.
Qian Shao +2 more
openaire +4 more sources
Constraints Driven Safe Reinforcement Learning for Autonomous Driving Decision-Making
IEEE AccessAlthough reinforcement learning (RL) methodologies exhibit potential in addressing decision-making and planning problems in autonomous driving, ensuring the safety of the vehicle under all circumstances remains a formidable challenge in practical ...
Fei Gao +4 more
doaj +1 more source
Advanced Robotics Research, EarlyView.A soft robotic simulator is developed to replicate the digital removal of feces (DRF), a sensitive yet essential nursing procedure. Integrating soft actuators, sensors, and a realistic rectal model, the simulator balances functional fidelity with perceptual realism. Engineering evaluations and nurse feedback confirm its potential to enhance training in
Shoko Miyagawa +10 more
wiley +1 more source
Reinforcement Learning Dynamics in Social Dilemmas [PDF]
In this paper we replicate and advance Macy and Flache\'s (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2 2 (2-player 2-strategy) social dilemmas.
Luis R. Izquierdo +2 more
core
Polynomial-Time Approximability of Constrained Reinforcement Learning
CoRRWe study the computational complexity of approximating general constrained Markov decision processes. Our primary contribution is the design of a polynomial time $(0,ε)$-additive bicriteria approximation algorithm for finding optimal constrained policies across a broad class of recursively computable constraints, including almost-sure, chance ...
openaire +4 more sources
Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
CoRR12 pages, 6 figures, supplementary material ...
Jialiang Fan +3 more
openaire +2 more sources
The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?
Advanced Robotics Research, EarlyView.Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley +1 more source
Constrained Exploration in Reinforcement Learning
Exploration sous contrainte dans l'apprentissage par renforcement Une application majeure de l'apprentissage machine automatisée est la personnalisation des différents contenus recommandé à différents utilisateurs. Généralement, les algorithmes étant à la base de ces systèmes sont dit supervisé.
openaire +2 more sources
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
Advanced Robotics Research, EarlyView.BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source
Nonlocomotory Robotic Strategies for Dynamic Rotation Control in Terrestrial Robots: A Review
Advanced Robotics Research, EarlyView.Terrestrial robots increasingly require rapid body rotation to maintain stability and agility in complex environments. This review shows nonlocomotory rotational control strategies that operate without ground contact, including reaction wheels, tails, bars, limbs, and thrusters.
Y. Liang +14 more
wiley +1 more source