# On the Optimality of Batch Policy Optimization Algorithms

@inproceedings{Xiao2021OnTO, title={On the Optimality of Batch Policy Optimization Algorithms}, author={Chenjun Xiao and Yifan Wu and Tor Lattimore and Bo Dai and Jincheng Mei and Lihong Li and Csaba Szepesv{\'a}ri and Dale Schuurmans}, booktitle={International Conference on Machine Learning}, year={2021} }

Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment. Although interest in this problem has grown significantly in recent years, its theoretical foundations remain underdeveloped. To advance the understanding of this problem, we provide three results that characterize the limits and possibilities of batch policy optimization in the finite-armed stochastic bandit setting. First, we introduce a class of confidenceadjusted…

## 17 Citations

### Model Selection in Batch Policy Optimization

- Computer ScienceICML
- 2022

It is shown that nobatch policy optimization algorithm can achieve a guarantee addressing all three sources of error simultaneously, revealing a stark contrast between difﬁculties in batch policy optimization and the positive results available in supervised learning.

### Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

- Computer ScienceICLR
- 2022

This paper proposes a provably efficient offline contextual bandit with neural network function approximation that does not require any functional assumption on the reward and shows that the method provably generalizes over unseen contexts under a milder condition for distributional shift than the existing OPL works.

### On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data

- Computer ScienceArXiv
- 2021

We study the fundamental question of the sample complexity of learning a good policy in nite Markov decision processes (MDPs) when the data available for learning is obtained by following a logging…

### Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL

- MathematicsNeurIPS
- 2021

Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the applications involving a large population of homogeneous agents, as it exploits the permutation invariance of agents and…

### Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings

- Computer ScienceNeurIPS
- 2021

This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified framework towards…

### TOWARDS THEORETICAL UNDERSTANDINGS OF ROBUST MARKOV DECISION PROCESSES: SAMPLE COMPLEXITY AND ASYMPTOTICS

- Mathematics
- 2022

In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes (MDPs), where the optimal robust policy and…

### Towards Instance-Optimal Offline Reinforcement Learning with Pessimism

- Computer ScienceNeurIPS
- 2021

This work analyzes the Adaptive Pessimistic Value Iteration (APVI) algorithm and derive the suboptimality upper bound that nearly matches O H ∑ in this work.

### Non-asymptotic Performances of Robust Markov Decision Processes

- MathematicsArXiv
- 2021

This paper considers three different uncertainty sets including the L1, χ 2 and KL balls in both (s, a)-rectangular and s-rectangular assumptions to find the non-asymptotic performance of optimal policy on robust value function with true transition dynamics.

### Characterizing Uniform Convergence in Offline Policy Evaluation via model-based approach: Offline Learning, Task-Agnostic and Reward-Free

- Computer Science
- 2021

An Ω(HS/dmǫ ) lower bound (over model-based family) for the global uniform OPE, where dm is the minimal state-action distribution induced by the behavior policy, is established and implies the optimal sample complexity for offline learning and separates local uniform O PE from the global case.

### ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data

- Computer ScienceArXiv
- 2022

In theory, it is proved that the learned policy of ARMOR never degrades the performance of the baseline policy with any admissible hyperparameter, and can learn to compete with the best policy within data coverage when thehyperparameter is well tuned, and the baselinepolicy is supported by the data.

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