Cover Image

Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hoang, Huy, Mai, Tien, Varakantham, Pradeep
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning
by: Hoang, Huy, et al.
Published: (2024)

UNIQ: Offline Inverse Q-learning for Avoiding Undesirable Demonstrations
by: Hoang, Huy, et al.
Published: (2024)

Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations
by: Hoang, Huy, et al.
Published: (2025)

Imitating Cost-Constrained Behaviors in Reinforcement Learning
by: Shao, Qian, et al.
Published: (2024)

Offline Safe Reinforcement Learning Using Trajectory Classification
by: Gong, Ze, et al.
Published: (2024)

Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
by: Jiang, Hao, et al.
Published: (2023)

On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
by: Ge, Zichang, et al.
Published: (2025)

Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning
by: Lu, Yuxiao, et al.
Published: (2023)

Regret-Based Defense in Adversarial Reinforcement Learning
by: Belaire, Roman, et al.
Published: (2023)

Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling
by: Li, Dexun, et al.
Published: (2023)

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
by: Baharav, Tavor Z., et al.
Published: (2025)

Automatic LLM Red Teaming
by: Belaire, Roman, et al.
Published: (2025)

On Minimizing Adversarial Counterfactual Error in Adversarial RL
by: Belaire, Roman, et al.
Published: (2024)

Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
by: Wu, Zhaomin, et al.
Published: (2025)

MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations
by: Bui, The Viet, et al.
Published: (2025)

Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
by: Banerjee, Debangshu, et al.
Published: (2023)

Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning
by: Nguyen, Khanh, et al.
Published: (2024)

Imitation Bootstrapped Reinforcement Learning
by: Hu, Hengyuan, et al.
Published: (2023)

On Discovering Algorithms for Adversarial Imitation Learning
by: Chirra, Shashank Reddy, et al.
Published: (2025)

SafeMIL: Learning Offline Safe Imitation Policy from Non-Preferred Trajectories
by: Burnwal, Returaj, et al.
Published: (2025)

IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health
by: Jain, Gauri, et al.
Published: (2024)

Of Good Demons and Bad Angels: Guaranteeing Safe Control under Finite Precision
by: Teuber, Samuel, et al.
Published: (2025)

RILe: Reinforced Imitation Learning
by: Albaba, Mert, et al.
Published: (2024)

Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning
by: Vaskov, Sean, et al.
Published: (2024)

Reinforcement Learning via Implicit Imitation Guidance
by: Dong, Perry, et al.
Published: (2025)

When Bad Data Leads to Good Models
by: Li, Kenneth, et al.
Published: (2025)

Offline Safe Policy Optimization From Heterogeneous Feedback
by: Gong, Ze, et al.
Published: (2025)

Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach
by: Gond, Bishwajit Prasad
Published: (2025)

Ambient Diffusion Omni: Training Good Models with Bad Data
by: Daras, Giannis, et al.
Published: (2025)

Blending Imitation and Reinforcement Learning for Robust Policy Improvement
by: Liu, Xuefeng, et al.
Published: (2023)

An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning
by: Xu, Haoran, et al.
Published: (2025)

FADE: Why Bad Descriptions Happen to Good Features
by: Puri, Bruno, et al.
Published: (2025)

Adaptive Primal-Dual Method for Safe Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2024)

Momentum Contrastive Learning with Enhanced Negative Sampling and Hard Negative Filtering
by: Hoang, Duy, et al.
Published: (2025)

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

Probabilistic Shielding for Safe Reinforcement Learning
by: Court, Edwin Hamel-De le, et al.
Published: (2025)

OSIL: Learning Offline Safe Imitation Policies with Safety Inferred from Non-preferred Trajectories
by: Burnwal, Returaj, et al.
Published: (2026)

SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
by: Anisimov, Maksim, et al.
Published: (2026)

On Generalization Across Environments In Multi-Objective Reinforcement Learning
by: Teoh, Jayden, et al.
Published: (2025)

A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
by: Koursioumpas, Nikolaos, et al.
Published: (2023)