TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
Authors: Tom Bewley, Jonathan Lawry11415-11422
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We initially validate TRIPLETREE in a simple MDP with 2 state features and 2 discrete actions... Figure 3 shows the result of growing a TRIPLETREE of up to 200 leaves using these four datasets, with various impurity weightings θ... Figure 7 shows how the three losses vary during growth on both the training set and a validation set. |
| Researcher Affiliation | Academia | Tom Bewley and Jonathan Lawry Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom {tom.bewley, j.lawry}@bristol.ac.uk |
| Pseudocode | No | The paper describes the algorithms and their modifications using text and mathematical equations, but it does not provide formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | A Python implementation of TRIPLETREE is available on Git Hub at https://github.com/tombewley/Triple Tree. |
| Open Datasets | No | For each, we create a dataset D with 10^4 samples, by running randomly-initialised episodes of 100 timesteps. Using a dataset of 10^5 observations, we grow a TRIPLETREE....The paper describes generating its own datasets and does not provide specific access information (link, DOI, or citation with authors/year for a public dataset) for these datasets. While OpenAI Gym is mentioned, the generated observations are not made available. |
| Dataset Splits | No | Figure 7 shows how the three losses vary during growth on both the training set and a validation set. The paper mentions training and validation sets but does not provide specific details on the dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'A Python implementation' and 'Open AI Gym' and cites 'Baselines Zoo', but does not provide specific version numbers for any of these software dependencies or other libraries. |
| Experiment Setup | Yes | For a given Rleft, Rright, Rspeed, discount factor γ (we use γ = 0.99), and suitable discretisation of S (we use a 30 30 grid)...For each, we create a dataset D with 10^4 samples, by running randomly-initialised episodes of 100 timesteps.Using a dataset of 10^5 observations, we grow a TRIPLETREE of up to 1000 leaves with θ = [1, 1, 1]...We use the validation losses to inform early stopping and select the 450-leaf tree for evaluation. |