Model-Based Control with Sparse Neural Dynamics
Authors: Ziang Liu, Genggeng Zhou, Jeff He, Tobia Marcucci, Fei-Fei Li, Jiajun Wu, Yunzhu Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical and hardware experiments show that, despite the aggressive sparsification, our framework can deliver better closed-loop performance than existing state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Cornell University 2Stanford University 3Massachusetts Institute of Technology 4University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper describes procedures in text and equations, but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions 'Please see our website at robopil.github.io/Sparse-Dynamics/ for additional visualizations.' but does not explicitly state that the source code for their method is available at this link or elsewhere. |
| Open Datasets | Yes | For closed-loop control evaluation, we additionally present the performance of our framework on two standardized benchmark environments from Open AI Gym [7], Cart Pole-v1 and Reacher-v4. |
| Dataset Splits | No | The paper describes data collection for training (e.g., '1,600 transition pairs') and mentions evaluating 'long-horizon predictive capability' but does not explicitly provide details about specific train/validation/test dataset splits (e.g., percentages or sample counts) for their collected data or how they set up validation for the OpenAI Gym environments. |
| Hardware Specification | No | The paper mentions 'Numerical and hardware experiments' but does not provide any specific details about the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | Yes | We generated 50,000 transition pairs using the Pymunk simulator [6]. The formulation in Equation 11 is the simplest mixed-integer encoding of a Re LU network, and a variety of strategies are available in the literature to accelerate the solution of our MIPs. ... and solve the problem using a commercial optimization solver, Gurobi [18]. Post-pruning, model speedup is performed using Neural Network Intelligence Library [51]. |
| Experiment Setup | Yes | Instead of limiting the number of regular Re LUs from the very beginning of the training process, we start with a randomly initialized neural network and use gradient descent to optimize ω and π by minimizing the following objective function until convergence: E[L(θ)] + λR(π), where the regularization term R(π) aims to explicitly reduce the use of the regular Re LU function. ...We then take an iterative approach by starting with a relatively large ε1 and gradually decrease its value for K iterations with ε1 > ε2 > > εK = ε. ...This yields a spectrum of models with varying degrees of sparsification. |