ContraBAR: Contrastive Bayes-Adaptive Deep RL
Authors: Era Choshen, Aviv Tamar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our third contribution is an empirical evaluation of our method that exposes several advantages of the contrastive learning approach. |
| Researcher Affiliation | Academia | 1Technion, Haifa, Israel. Correspondence to: Era Choshen <erachoshen@campus.technion.ac.il>. |
| Pseudocode | No | The paper describes the Contra BAR algorithm and its architecture using text and diagrams (e.g., Figure 1) but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | exact hyperparameters can be found in our code at https://github.com/ec2604/Contra BAR |
| Open Datasets | No | The paper describes using specific environments for training, such as MuJoCo locomotion tasks and custom image-based environments (Reacher-Image, Panda Reacher, Panda Wind). While these are based on existing simulators, the paper does not provide concrete access (link, DOI, citation with author/year) to the specific datasets collected or used for training these experiments. |
| Dataset Splits | No | The paper discusses 'training tasks' and evaluating 'test performance' but does not explicitly provide details about training/validation/test dataset splits, such as specific percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments, such as GPU models, CPU types, or memory details. It only mentions general computational aspects like 'memory limitations'. |
| Software Dependencies | No | The paper mentions using algorithms like PPO (Schulman et al., 2017) and SAC (Haarnoja et al., 2018), but it does not specify version numbers for any software dependencies, libraries, or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | The paper provides specific details about the experimental setup within the environment descriptions, such as: 'The horizon is set to 50', 'radius r = 0.05', 'action space is 3-dimensional and bounded [ -1, 1]3', 'images of size 64 x 64'. It also explicitly states: 'exact hyperparameters can be found in our code at https://github.com/ec2604/Contra BAR'. |