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.
Sampling-Based Safe Reinforcement Learning
Sampling-Based Safe Reinforcement Learning (SBSRL) is a model‑based RL algorithm that enforces safety by jointly constraining a finite set of dynamics samples, approximating a worst‑case optimization over uncertain dynamics. It introduces an exploration strategy that limits epistemic uncertainty, removing the need for explicit exploration bonuses. The authors provide high‑probability safety guarantees and a finite‑time sample complexity bound, and demonstrate safe, efficient exploration in both simulation and real robotic hardware, with extensions to deep‑ensemble implementations for high‑dimensional continuous control.
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arXiv Machine Learning
Jun 2
All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning
arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.
arXiv Machine Learning
Sep 3
Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.
arXiv AI
Sep 2
Provably Safe Sim-to-Real Transfer
arXiv:2609.01418v1 Announce Type: cross Abstract: To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where sampl...
arXiv AI
Jun 10
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.
arXiv Machine Learning
Aug 11
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.
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
Oct 5
Offline Policy Optimization with Posterior Sampling
The paper introduces PSPO, a method for offline policy optimization that treats the dynamics model as a random variable, enabling controlled exploration of out‑of‑distribution (OOD) regions while maintaining robustness. By alternating updates of the posterior distribution and the policy, PSPO achieves a regularized optimization algorithm with convergence guarantees. Experiments on standard benchmarks show that PSPO outperforms state‑of‑the‑art baselines and retains a pessimism‑free property without sacrificing robustness.
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.
Hugging Face Trending Papers
Jul 8
Safe Reinforcement Learning using Ideas from Model Predictive Control
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
arXiv Machine Learning
Aug 24
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models
The paper introduces BUMEX, a reinforcement learning exploration strategy that leverages a set of prior models containing the true transition kernel and reward function. By optimizing over this model set, the method derives upper and lower bounds on the Q‑function to guide exploration, providing theoretical guarantees of convergence to the optimal policy. When the model set follows a bounded‑parameter MDP structure, the optimization becomes convex, enabling finite‑time convergence under mild assumptions and demonstrating accelerated learning in simulations.
arXiv Machine Learning
Jul 15
Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
arXiv:2607. 12784v1 Announce Type: cross Abstract: Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation.