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..
Reinforcement Learning with a Terminator
Authors: Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We deploy our method on high-dimensional driving and Min Atar benchmarks. Additionally, we test our approach on human data in a driving setting. Our results demonstrate fast convergence and significant improvement over various baseline approaches. |
| Researcher Affiliation | Collaboration | Technion, Israel institute of technology Nvidia Research, Israel Bar Ilan University, Israel |
| Pseudocode | Yes | Algorithm 1 Term CRL: Termination Confidence Reinforcement Learning; Algorithm 2 Term PG |
| Open Source Code | Yes | Code for Backseat Driver and our method, Term PG, can be found at https://github.com/guytenn/Terminator. |
| Open Datasets | Yes | We further compared our method to the PG, recurrent PG, and reward shaping methods, on Min Atar [Young and Tian, 2019]. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | All experiments ran on our machine with 4 NVIDIA GeForce RTX 3090 GPUs and Intel Core i9-10900X CPU. |
| Software Dependencies | No | The paper mentions using 'MLAgents [Juliani et al., 2018]' and 'RLlib [Liang et al., 2018]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Min Atar experiments ran for 5 million time steps with a learning rate of 5e-4 and a batch size of 2048. Backseat Driver experiments ran for 15 million time steps with a learning rate of 1e-4 and a batch size of 2048. |