arXiv AI
Jun 16
Safe Exploration via Policy Priors
arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.
Computationally efficient safe exploration in reinforcement learning
The paper introduces “CoLSafe-MDP”, a reinforcement learning algorithm that ensures safe exploration in constrained Markov decision processes. It replaces computationally heavy Gaussian process methods with a Nadaraya-Watson estimator, achieving constant-time scaling for estimate bounds. The authors evaluate the algorithm on a grid-based environment and on observational Martian terrain data.
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arXiv Machine Learning
Aug 17
Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
arXiv:2608. 14466v1 Announce Type: cross Abstract: An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes.
arXiv AI
Aug 21
Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning
arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).
arXiv Machine Learning
Sep 18
Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning
The paper introduces a model-based bootstrap framework for uncertainty quantification in offline policy evaluation (OPE) within finite-horizon, time-inhomogeneous Markov decision processes. Unlike traditional bootstrap methods that resample entire episodes, this approach regenerates trajectories from an estimated MDP, enabling use of diverse offline data formats such as complete trajectories, transition-level observations, and trajectory fragments. The authors prove bootstrap distributional consistency, asymptotically valid confidence intervals, and consistent variance estimation, and demonstrate through simulations that the method yields tighter confidence intervals and more accurate variance estimates compared to existing techniques.
arXiv AI
Jul 3
Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.
arXiv Machine Learning
Aug 3
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
arXiv:2307. 10524v3 Announce Type: replace Abstract: We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice.
arXiv Machine Learning
Jul 20
Robust Peak-cost Constrained Reinforcement Learning
arXiv:2607. 15457v1 Announce Type: new Abstract: We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a trajectory.
arXiv Machine Learning
Jul 21
Information-Based Exploration via Random Features for Reinforcement Learning
arXiv:2607. 17981v1 Announce Type: new Abstract: Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish.
arXiv AI
Jun 19
Flickering Multi-Armed Bandits
arXiv:2602. 17315v3 Announce Type: replace-cross Abstract: We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is restricted to a subset dependent on the agent's current choice.
arXiv Machine Learning
Jul 1
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
arXiv Machine Learning
Jun 11
Reinforcement Learning with Action-Triggered Observations
arXiv:2510. 02149v2 Announce Type: replace Abstract: We introduce Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), a reinforcement learning framework for partial observability in which full state observations occur stochastically at each step, with probability determined by the chosen action.
arXiv Machine Learning
Jun 30
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.