Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Provably Efficient CVaR RL in Low-rank MDPs
Authors: Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that our algorithm achieves a sample complexity of O H7A2d4 τ 2ϵ2 to yield an ϵ-optimal CVa R, where H is the length of each episode, A is the capacity of action space, and d is the dimension of representations. Computational-wise, we design a novel discretized Least-Squares Value Iteration (LSVI) algorithm for the CVa R objective as the planning oracle and show that we can find the near-optimal policy in a polynomial running time with a Maximum Likelihood Estimation oracle. To our knowledge, this is the first provably efficient CVa R RL algorithm in low-rank MDPs. |
| Researcher Affiliation | Academia | Yulai Zhao Princeton University EMAIL Wenhao Zhan Princeton University EMAIL Xiaoyan Hu The Chinese University of Hong Kong EMAIL Ho-fung Leung Independent Researcher EMAIL Farzan Farnia The Chinese University of Hong Kong EMAIL Wen Sun Cornell University EMAIL Jason D. Lee Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 ELA and Algorithm 3 ELLA are provided as structured pseudocode blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not specify the use of any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameter values or training configurations. |