Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, creating path-dependent noise. To understand…
Continuous ControlFastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensionality'' induces severe exploration inefficiency and training instabilit…
Reinforcement LearningContinuous ControlHybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
Deep reinforcement learning for continuous control often suffers from high variance, low energy efficiency, and poor generalization under distribution shift, as purely data-driven exploration ignores available physical s…
Reinforcement LearningContinuous ControlTaming the Adversary: Stable Minimax Deep Deterministic Policy Gradient via Fractional Objectives
Reinforcement learning (RL) has achieved remarkable success in a wide range of control and decision-making tasks. However, RL agents often exhibit unstable or degraded performance when deployed in environments subject to…
Reinforcement LearningContinuous ControlAdaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents
The integration of Generative AI models into AI-native network systems offers a transformative path toward achieving autonomous and adaptive control. However, the application of such models to continuous control tasks is…
Reinforcement LearningContinuous ControlResWM: Residual-Action World Model for Visual RL
Learning predictive world models from raw visual observations is a central challenge in reinforcement learning (RL), especially for robotics and continuous control. Conventional model-based RL frameworks directly conditi…
Reinforcement LearningContinuous ControlCogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation
Beyond conveying semantic information, images also possess cognitive properties that elicit specific psychological responses from viewers, such as memory encoding or emotional reactions. Although modern text-to-image (T2…
Text-to-Image GenerationContinuous ControlPosterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm. Posterior sampling is a heuristic for decision-making under uncertainty that has been used to develop …
Reinforcement LearningContinuous ControlGaussian ProcessesTowards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limite…
Reinforcement LearningContinuous ControlGlobal Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization
We study infinite-horizon Constrained Markov Decision Processes (CMDPs) with general policy parameterizations and multi-layer neural network critics. Existing theoretical analyses for constrained reinforcement learning l…
Reinforcement LearningContinuous ControlSTL-SVPIO: Signal Temporal Logic guided Stein Variational Path Integral Optimization
Signal Temporal Logic (STL) enables formal specification of complex spatiotemporal constraints for robotic task planning. However, synthesizing long-horizon continuous control trajectories from complex STL specifications…
Continuous ControlMotion PlanningSynthetic Monitoring Environments for Reinforcement Learning
Reinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexity factors and lack ground-truth optimality metrics, making it diffic…
Reinforcement LearningContinuous ControlCognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport
Effective human-robot collaboration (HRC) requires translating high-level intent into contact-stable whole-body motion while continuously adapting to a human partner. Many vision-language-action (VLA) systems learn end-t…
Continuous ControlSelf-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback
As learning-based robotic controllers are typically trained offline and deployed with fixed parameters, their ability to cope with unforeseen changes during operation is limited. Biologically inspired, this work presents…
Reinforcement LearningContinuous ControlPost Hoc Extraction of Pareto Fronts for Continuous Control
Agents in the real world must often balance multiple objectives, such as speed, stability, and energy efficiency in continuous control. To account for changing conditions and preferences, an agent must ideally learn a Pa…
Reinforcement LearningContinuous ControlState-Action Inpainting Diffuser for Continuous Control with Delay
Signal delay poses a fundamental challenge in continuous control and reinforcement learning (RL) by introducing a temporal gap between interaction and perception. Current solutions have largely evolved along two distinct…
Reinforcement LearningContinuous ControlOffline RLPrincipled Fast and Meta Knowledge Learners for Continual Reinforcement Learning
Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual-learner framework comprising a fast learner and a meta learner to addre…
Reinforcement LearningContinuous ControlContinual LearningThe Informational Cost of Agency: A Bounded Measure of Interaction Efficiency for Deployed Reinforcement Learning
Deployed reinforcement learning systems lack a principled runtime reliability theory. We close this gap by introducing Bipredictability, P, a closed form information theoretic metric that quantifies how efficiently a clo…
Reinforcement LearningContinuous ControlDistilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local …
Reinforcement LearningContinuous ControlIn-Context Planning with Latent Temporal Abstractions
Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long horizons, while real environments are freque…
Reinforcement LearningContinuous ControlOffline RL