Cover Image

Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	McCarthy, James, Marinescu, Radu, Daly, Elizabeth, Dusparic, Ivana
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning
by: Rajapakse, Dilina, et al.
Published: (2026)

Adapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward Functions
by: de la Rosa, Raul, et al.
Published: (2026)

ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
by: Gajcin, Jasmina, et al.
Published: (2024)

Continual Reinforcement Learning for Cyber-Physical Systems: Lessons Learned and Open Challenges
by: Nolle, Kim N., et al.
Published: (2025)

Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
by: Wang, Wenlong, et al.
Published: (2024)

Optimistic Reinforcement Learning with Quantile Objectives
by: Alipour-Vaezi, Mohammad, et al.
Published: (2025)

Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
by: Mete, Akshay, et al.
Published: (2026)

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
by: Gajcin, Jasmina, et al.
Published: (2022)

Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
by: Yue, Bo, et al.
Published: (2024)

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
by: Hou, Yufang, et al.
Published: (2024)

Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
by: Tomar, Shivani, et al.
Published: (2026)

RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
by: Hau, Jia Lin, et al.
Published: (2022)

General Exploratory Bonus for Optimistic Exploration in RLHF
by: Li, Wendi, et al.
Published: (2025)

Semifactual Explanations for Reinforcement Learning
by: Gajcin, Jasmina, et al.
Published: (2024)

Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by: Vakili, Sattar, et al.
Published: (2024)

Conflict-Averse Gradient Descent for Multi-task Learning
by: Liu, Bo, et al.
Published: (2021)

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
by: Moustafa, Emran Yasser, et al.
Published: (2025)

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
by: Kiyani, Shayan, et al.
Published: (2025)

Optimistic Rates for Learning from Label Proportions
by: Li, Gene, et al.
Published: (2024)

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
by: Ying, Chengyang, et al.
Published: (2022)

Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
by: Zhao, Chenyang, et al.
Published: (2026)

Distilling Symbolic Priors for Concept Learning into Neural Networks
by: Marinescu, Ioana, et al.
Published: (2024)

Optimistic Policy Regularization
by: Pham, Mai, et al.
Published: (2026)

Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
by: Rosero, Juan C., et al.
Published: (2024)

Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
by: Lee, Jane H., et al.
Published: (2025)

Anytime-Constrained Reinforcement Learning
by: McMahan, Jeremy, et al.
Published: (2023)

Off-Policy Safe Reinforcement Learning with Constrained Optimistic Exploration
by: Li, Guopeng, et al.
Published: (2026)

In-context Exploration-Exploitation for Reinforcement Learning
by: Dai, Zhenwen, et al.
Published: (2024)

Satisficing Exploration for Deep Reinforcement Learning
by: Arumugam, Dilip, et al.
Published: (2024)

Reinforcement Learning by Guided Safe Exploration
by: Yang, Qisong, et al.
Published: (2023)

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by: Moon, Sang Bin, et al.
Published: (2024)

Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
by: Ganguly, Sourav, et al.
Published: (2026)

Continual Learning as Computationally Constrained Reinforcement Learning
by: Kumar, Saurabh, et al.
Published: (2023)

State-Constrained Offline Reinforcement Learning
by: Hepburn, Charles A., et al.
Published: (2024)

Locally Constrained Representations in Reinforcement Learning
by: Nath, Somjit, et al.
Published: (2022)

Driving Through Uncertainty: Risk-Averse Control with LLM Commonsense for Autonomous Driving under Perception Deficits
by: Hu, Yuting, et al.
Published: (2025)

Polynomial-Time Approximability of Constrained Reinforcement Learning
by: McMahan, Jeremy
Published: (2025)

Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
by: Berseth, Glen
Published: (2025)

Neighboring State-based Exploration for Reinforcement Learning
by: Li, Yu-Teng, et al.
Published: (2022)

Variable-Agnostic Causal Exploration for Reinforcement Learning
by: Nguyen, Minh Hoang, et al.
Published: (2024)