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..
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Authors: Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions. |
| Researcher Affiliation | Collaboration | Tom Zahavy 1,2, Matan Haroush 1, Nadav Merlis 1, Daniel J. Mankowitz3, Shie Mannor1 1The Technion Israel Institute of Technology, 2 Google research, 3 Deepmind |
| Pseudocode | Yes | Algorithm 1 deep Q-learning with action elimination |
| Open Source Code | Yes | Our code, the Zork domain, and the implementation of the elimination signal can be found at: https://github.com/TomZahavy/CB_AE_DQN |
| Open Datasets | Yes | Our code, the Zork domain, and the implementation of the elimination signal can be found at: https://github.com/TomZahavy/CB_AE_DQN |
| Dataset Splits | No | The paper describes training and evaluation protocols (e.g., discounted factor for training vs. evaluation) but does not provide explicit training/validation/test dataset splits as commonly understood in supervised learning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific models like 'NLP CNN' and 'word2vec', but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set the discounted factor during training to γ = 0.8 but use γ = 1 during evaluation 4. We used β = 0.5, ℓ= 0.6 in all the experiments. The results are averaged over 5 random seeds, shown alongside error bars (std/3). |