Learning Programmatically Structured Representations with Perceptor Gradients
Authors: Svetlin Penkov, Subramanian Ramamoorthy
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results confirm that the perceptor gradients algorithm is able to efficiently learn transferable symbolic representations as well as generate new observations according to a semantically meaningful specification. |
| Researcher Affiliation | Collaboration | Svetlin Penkov 1, 2, , Subramanian Ramamoorthy 1, 2 1The University of Edinburgh, 2Five AI {sv.penkov, s.ramamoorthy}@ed.ac.uk |
| Pseudocode | Yes | Algorithm 1: Perceptor rollout for a single episode |
| Open Source Code | No | The paper mentions using third-party packages like 'python-control' and 'python-astar', providing links to them. However, it does not state that the authors' own implementation code for the perceptor gradients algorithm is open-source or publicly available. |
| Open Datasets | No | For the cart-pole experiment, the paper states 'we generated a dataset of observations obtained by controlling the cart-pole with the perceptor for 100 episodes.' For Minecraft, the environment is described without mention of a specific public dataset or access information for the generated data. The paper uses OpenAI Gym and a Minecraft-like environment, but doesn't provide specific access to the *data* they used for training. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages, sample counts, or citations to predefined splits for training, validation, and testing datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'python-control' and 'python-astar' packages and 'Open AI gym' but does not specify their version numbers or other software dependencies with explicit versioning (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We set the program ρ to implement an LQR. The perceptor ψθ is a convolutional neural network (see A.1)... Q = 103 I4 and R = 1... producing 2 discrete actions required by the Open AI gym cart-pole environment. ... we set the program ρ to be ρ(σ) = a = 1 if Kσ > 0 0 otherwise... We train both perceptors jointly, but keep the learning rate for the pre-trained pose perceptor considerably lower than the one for the wood perceptor. |