Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,431편 · 필터 해제
Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation
Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navigation (VLN) benchmarks typically use di…
Vision-Language NavigationContinuous ControlActionMap: Robot Policy Learning via Voxel Action Heatmap
Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state into a continuous control signal, has barel…
Continuous ControlRepresentation Learning Enables Scalable Multitask Deep Reinforcement Learning
Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, mak…
Representation LearningReinforcement LearningContinuous ControlConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transf…
Reinforcement LearningContinuous ControlFrom Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control. Build…
Reinforcement LearningContinuous ControlWhen are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorithms with an LLM? We explore this question…
Reinforcement LearningContinuous ControlZero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments
Bidding in repeated auctions is a central challenge for reinforcement learning (RL), combining continuous control with the strategic complexities of digital advertising. While policy gradient and value-based methods seem…
Reinforcement LearningContinuous ControlContinuous Reasoning for Vision-Language-Action
Natural language is a powerful reasoning medium for language and vision-language models, but it is mismatched to the granularity of continuous control. Text and explicit subgoals operate at task-level granularity, wherea…
Continuous ControlZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning
Continuous control policies trained with off-policy reinforcement learning frequently exhibit high-frequency action jitter, impractical for direct deployment on physical actuators. Post-hoc filtering attenuates jitter bu…
Reinforcement LearningContinuous ControlBeyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models
Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rely on Gumbel-Softmax to approximate discrete selection during trainin…
Continuous ControlTheoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably exploit minor model inaccuracies, leading to simulator exploitation …
Reinforcement LearningContinuous ControlRatio-Variance Regularized Policy Optimization
Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscriminately truncating high-return yet high-divergence updates. We demonst…
Reinforcement LearningMathematical ReasoningContinuous ControlStreaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.e. with a batch size of 1 and no replay buffer. …
Reinforcement LearningContinuous ControlReflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes ($G$-invariant MDPs). Existing works in this …
Reinforcement LearningContinuous ControlOpenAI GymGoal-Conditioned Agents that Learn Everything All at Once
A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is discarded when only performing on-policy updates with respect to the com…
Reinforcement LearningContinuous ControlKernel-Based Safe Exploration in Deep Reinforcement Learning
Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the learned policy does not visit unsafe regions is to learn a \emph{barrie…
Reinforcement LearningContinuous ControlDreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do not explicitly enforce local smoothness in their learned transition dyna…
Reinforcement LearningContinuous ControlCIG: Exploration via Conditional Information Gain
Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within-rollout redundancy; episodic rewards pe…
Reinforcement LearningContinuous ControlPREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
We address the problem of making a pre-trained reinforcement learning (RL) policy safety-aware by incorporating cost constraints without retraining it from scratch. While costs could be numerically encoded, we assume a m…
Computational EfficiencyReinforcement LearningContinuous ControlDecision MakingRebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often requ…
Continuous Control