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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Risk-averse Total-reward MDPs with ERM and EVaR
Authors: Xihong Su, Marek Petrik, Julien Grand-Clément
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effect of risk-aversion on the structure of the optimal policy, we use the gambler s ruin problem (Hau, Petrik, and Ghavamzadeh 2023; B auerle and Ott 2011). In this problem, a gambler starts with a given amount of capital and seeks to increase it up to a cap K. ... The algorithm was implemented in Julia 1.10, and is available at https://github.com/suxh2019/ERMLP. Please see Su, Grand Cl ement, and Petrik (2024, appendix F) for more details. Figure 3 shows optimal policies for four different EVa R risk levels α computed by Algorithm 1. ... To understand the impact of risk-aversion on the distribution of returns, we simulate the resulting policies over 7,000 episodes and show the distribution of capitals in Figure 4. |
| Researcher Affiliation | Academia | 1University of New Hampshire, 33 Academic Way, Durham, NH, 03824 USA 2 HEC Paris, 1 Rue de la Lib eration, Jouy-en-Josas, 78350 France EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Simple EVa R algorithm |
| Open Source Code | Yes | The algorithm was implemented in Julia 1.10, and is available at https://github.com/suxh2019/ERMLP. |
| Open Datasets | No | The paper uses the 'gambler's ruin problem' as a simulation environment with specified parameters (q=0.68, K=7) and simulates policies over 7,000 episodes. This is a self-generated simulated dataset, not a publicly available external dataset with access information. |
| Dataset Splits | No | The paper describes a simulation study for the gambler's ruin problem where policies are simulated over 7,000 episodes. It does not mention any explicit training, validation, or test splits for a dataset, as the data is generated through simulation rather than being a pre-existing dataset that needs splitting. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions the implementation language. |
| Software Dependencies | Yes | The algorithm was implemented in Julia 1.10 |
| Experiment Setup | Yes | In the formulation, we use q = 0.68, and a cap is K = 7. ... Figure 3 shows optimal policies for four different EVa R risk levels α computed by Algorithm 1. ... To understand the impact of risk-aversion on the distribution of returns, we simulate the resulting policies over 7,000 episodes |