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..
Inverse Constrained Reinforcement Learning
Authors: Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our approach and show that our framework can successfully learn the most likely constraints that the agent respects. |
| Researcher Affiliation | Academia | 1Information Technology University, Lahore, Pakistan 2Georgia State University, Atlanta, GA, USA. |
| Pseudocode | Yes | Algorithm 1 ICRL |
| Open Source Code | Yes | The code is available at: https: //github.com/shehryar-malik/icrl. |
| Open Datasets | Yes | For this experiment, we use two simulated robots called Half Cheetah and Ant from Open AI Gym (Brockman et al., 2016). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes using environments from OpenAI Gym, which typically involve continuous interaction rather than predefined static datasets with explicit splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, or memory). |
| Software Dependencies | No | The paper mentions using Stable Baselines and Weights and Biases, but it does not specify version numbers for these or other core software components like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | Finally, all hyperparameters and additional details of the experiments can be found in Section 8.5 in the supplementary material. |