Learning sparse relational transition models
Authors: Victoria Xia, Zi Wang, Kelsey Allen, Tom Silver, Leslie Pack Kaelbling
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS We apply our approach, SPARE, to a challenging problem of predicting pushing stacks of blocks on a cluttered table top. We describe our domain, the baseline that we compare to and report our results. Figure 4: (a) In a simple 3-block pushing problem instance, data likelihood and learned default standard deviation both improve as more deictic references are added. (b) Comparing performance as a function of number of distractors with a fixed amount of training data. (c) Comparing sample efficiency of SPARE to the baselines. Shaded regions represent 95% confidence interval. |
| Researcher Affiliation | Academia | Victoria Xia Zi Wang Kelsey Allen Tom Silver Leslie Pack Kaelbling Equal contribution. Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139. {vxia,ziw,krallen,tslvr,lpk}@mit.edu. |
| Pseudocode | Yes | Algorithm 1 Greedy procedure for constructing Γ. |
| Open Source Code | No | The paper does not include any explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We simulate this 3D domain using the physically realistic Py Bullet (Coumans & Bai, 2016 2018) simulator. The paper describes generating data by sampling problem instances and action parameters, rather than using a pre-existing publicly available dataset with concrete access information. |
| Dataset Splits | Yes | We held out 20% of the training data as the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Keras' and 'Adam optimizer' without specific version numbers. It also refers to 'PyBullet (Coumans & Bai, 2016 2018)', which is a range and not a single, specific version number for reproducibility. |
| Experiment Setup | Yes | Predictors for the templates approach were trained for 1000 epochs each with a decaying learning rate starting at 1e-2 and decreasing by a factor of 0.6 every 100 epochs. The GNN was trained using a decaying learning rate starting at 1e-2, and decreasing by a factor of 0.5 every 100 epochs. A total of 900 epochs were used. |