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..
Rewiring Neurons in Non-Stationary Environments
Authors: Zhicheng Sun, Yadong Mu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed method is comprehensively evaluated on 18 continual reinforcement learning scenarios ranging from locomotion to manipulation, demonstrating its advantages over state-of-the-art competitors in performance-efficiency tradeoffs. Code is available at https://github.com/feifeiobama/Rewire Neuron. |
| Researcher Affiliation | Academia | Zhicheng Sun, Yadong Mu Peking University, Beijing, China EMAIL |
| Pseudocode | No | The paper describes its methods verbally and with figures, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/feifeiobama/Rewire Neuron. |
| Open Datasets | Yes | Environments. We use 18 continual reinforcement learning scenarios from Brax and Continual World: (1) Brax [18, 20] contains 9 locomotion scenarios over 3 domains: Half Cheetah, Ant and Humanoid. (2) Continual World [69] is a manipulation benchmark built on Meta-World [73] and Mu Jo Co [65], featuring 8 scenarios with 3 tasks (CW3) and one scenario with 10 tasks (CW10), both with a varying reward function and a budget of 1M interactions per task. More details are provided in Appendix A.1. |
| Dataset Splits | No | The paper does not provide explicit numerical or proportional splits (e.g., train/validation/test percentages or counts) for datasets used in the experiments. It mentions |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for experiments. |
| Software Dependencies | No | We build on the Sa Lin A library [12] and adopt Soft Actor-Critic (SAC) [25] with autotuned temperature [26] as the underlying algorithm. Both the actor and the critic are 4-layer perceptions with 256 hidden neurons per layer, while the actor also includes task-specific heads [69]. Their training configurations follow [20]. |
| Experiment Setup | Yes | Implementation details. We build on the Sa Lin A library [12] and adopt Soft Actor-Critic (SAC) [25] with autotuned temperature [26] as the underlying algorithm. Both the actor and the critic are 4-layer perceptions with 256 hidden neurons per layer, while the actor also includes task-specific heads [69]. Their training configurations follow [20]. For our method, we choose the new hyperparameters K, α, and β via grid search for each scenario, and provide a sensitivity analysis in Section 4.3. The score vectors in Eq. (4) are initialized with an arithmetic sequence rescaled to [0, 1], and the temperature is τ = 1 by default. |