arXiv Machine Learning
Jun 26
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
arXiv:2606. 26463v1 Announce Type: new Abstract: Deliberating takes time.
Adaptive Reinforcement Learning for Unobservable Random Delays
arXiv:2506. 14411v2 Announce Type: replace-cross Abstract: In standard reinforcement learning (RL) settings, the interaction between the agent and the environment is typically modeled as a Markov decision process (MDP), which assumes that the agent observes the system state instantaneously, selects an action without delay, and executes it immediately.
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Hugging Face Trending Papers
Jun 24
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Deliberating takes time. In real-time settings, that time is not free.
arXiv AI
Jul 7
Diffusion-Guided Uncertainty-Aware Delayed Policy Optimization
arXiv:2607. 05064v1 Announce Type: new Abstract: Reinforcement learning in real world environments often suffers from severe performance degradation due to delayed feedback.
arXiv Machine Learning
Jun 3
Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
arXiv:2603. 03480v2 Announce Type: replace Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps.
arXiv AI
6d ago
Let it Cook: Learning to Wait in Sequential Decision Making
arXiv:2608. 11511v1 Announce Type: cross Abstract: In sequential decision making, an agent typically observes its environment and acts at every timestep.
arXiv AI
Jul 21
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.
arXiv Machine Learning
Jul 9
Safe Reinforcement Learning using Ideas from Model Predictive Control
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
arXiv AI
Jun 17
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
arXiv:2606. 18223v1 Announce Type: cross Abstract: With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL).
arXiv Machine Learning
Jul 22
S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning
arXiv:2607. 19232v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution.
arXiv AI
Jul 31
Reinforcement Learning on Cost-Constrained Quadrupedal Hardware
arXiv:2607. 26434v2 Announce Type: cross Abstract: Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap.
arXiv AI
Jun 11
Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling
arXiv:2501. 12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities.
arXiv Machine Learning
Jul 21
Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search
arXiv:2603. 16842v2 Announce Type: replace Abstract: Stochastic resetting -- intermittently returning a process to a fixed reference state -- has emerged as an effective mechanism for optimizing first-passage properties.