Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world. Several recent works have demonstrated the ability to learn policies directly from offline data. In this work, we ask the question of the dependency on the number of samples for learning from offline data. Our objective is to emphasize that studying sample complexity for offline RL is important, and is an indicator of the usefulness of existing offline algorithms. We propose an evaluation approach for sample complexity analysis of offline RL.
Code (0)
등록된 구현이 없습니다.
Tasks
Offline RLreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples
Offline reinforcement learning (offline RL) considers problems where learning is performed using only previously collected samples and is helpful for the settings in which collecting new data is costly or risky. In model…
Offline RLOff-policy evaluationreinforcement-learningReinforcement Learning+1Optimal Best-Arm Identification in Bandits with Access to Offline Data
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature. In this paper, we consider combining offline data with online learning…
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Offline safe reinforcement learning (RL) is increasingly important for cyber-physical systems (CPS), where safety violations during training are unacceptable and only pre-collected data are available. Existing offline sa…
Reinforcement LearningOffline RLFederated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Offline reinforcement learning (RL), which seeks to learn an optimal policy using offline data, has garnered significant interest due to its potential in critical applications where online data collection is infeasible o…
Federated LearningOffline RLQ-Learningreinforcement-learning+2On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
Kullback-Leibler (KL) regularization is widely used in offline decision-making and offers several benefits, motivating recent work on the sample complexity of offline learning with respect to KL-regularized performance m…
Multi-Armed Bandits