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..
Reward Penalties on Augmented States for Solving Richly Constrained RL Effectively
Authors: Hao Jiang, Tien Mai, Pradeep Varakantham, Huy Hoang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Results We experimentally answer the following questions with regards to our approaches : |
| Researcher Affiliation | Academia | Singapore Management University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The pseudo code for the Safe DQN algorithm is provided in the appendix. [...] The detailed pseudocode for Safe SAC is provided in the appendix. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | Yes | For a discrete state and discrete action environment, we consider the stochastic 2D grid world problem introduced in previous CMDP works (Leike et al. 2017; Chow et al. 2018; Satija, Amortila, and Pineau 2020; Jain, Khetarpal, and Precup 2021). [...] Next, we consider the highway environment [...] (Leurent 2018). [...] We then compare Safe SAC with recent safe methods for continuous action spaces on the two environments Safety Point Goal1-v0, Safety Car Goal1-v0 from Safety Gymnasium (Ji et al. 2023). |
| Dataset Splits | Yes | The performance values (expected cost and expected reward) along with the standard deviation in each experiment are averaged over 5 runs. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | We set the expected cost threshold, cmax = 2, meaning agent could pass at most one pit. [...] We set the cmax = 8. [...] We set cmax = 15. [...] we conduct experiments on Grid World using Safe SAC, with λ1 = 1, λ2 = 5λ1, λ3 = 10λ1, a small λ4 = 0.001 and λ5 = 0 |