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..
Prediction and Control in Continual Reinforcement Learning
Authors: Nishanth Anand, Doina Precup
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, this approach improves performance significantly on both prediction and control problems. |
| Researcher Affiliation | Collaboration | Nishanth Anand School of Computer Science Mc Gill University and Mila EMAIL Doina Precup School of Computer Science Mc Gill University, Mila, and Deepmind EMAIL |
| Pseudocode | Yes | Algorithm 1 PT-TD learning (Prediction) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | Yes | Empirical case studies of the proposed approaches in simple gridworlds, Minigrid [11], Jelly Bean World (JBW) [31], and Min Atar environments [51]. |
| Dataset Splits | No | The paper describes episodic training and task changes (e.g., 'We run 750 episodes and change rewards every 75 episodes.') but does not specify explicit train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The discount factor is 0.9 in all cases. We run 750 episodes and change rewards every 75 episodes. We use epsilon-greedy policy with e = 0.1 for exploration. Experience replay buffer s capacity is capped to 100k. |