Search Results for author:
Found 4 papers, 3 papers with code
Learning from Sparse Offline Datasets via Conservative Density Estimation
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning
Leveraging insights from this framework and recognizing the significance of \textit{redundant} and \textit{conflicting} constraint conditions, we introduce the Gradient Shaping (GradS) method for general Lagrangian-based safe RL algorithms to improve the training efficiency in terms of both reward and constraint satisfaction.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment.
