Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Successor Feature Sets: Generalizing Successor Representations Across Policies
Authors: Kianté Brantley, Soroush Mehri, Geoff J. Gordon11774-11781
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to explore which of the potential barriers to scaling are most pressing. ... Experiments: Dynamic Programming We tried our dynamic programming method on several small domains: the classic mountain-car domain and a random 18 x 18 gridworld with full and partial observability. We evaluated both planning and feature matching; results for the former are discussed in this section, and an example of the latter is in Fig. 3. |
| Researcher Affiliation | Collaboration | Kiant e Brantley,1 Soroush Mehri, 2 Geoffrey J. Gordon 2 1 University of Maryland College Park 2 Microsoft Research |
| Pseudocode | Yes | Algorithm 1: Feature Matching Policy |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper uses simulated environments ('mountain-car domain' and '18x18 gridworld') but does not specify or provide access to a distinct, publicly available dataset in the format required (e.g., a link, DOI, or formal citation for a pre-existing dataset). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., exact percentages or sample counts), nor does it reference predefined splits with citations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers necessary for replication. |
| Experiment Setup | Yes | In mountain-car, the agent has two actions: accelerate left and accelerate right. The state is (position, velocity), in [-1.2, 0.6] x [-0.07, 0.07]. We discretize to a 12x12 mesh with piecewise-constant approximation. Our one-step features are radial basis functions of the state, with values in [0, 1]. We use 9 RBF centers evenly spaced on a 3x3 grid. ... In the MDP gridworld, the agent has four deterministic actions: up, down, left, and right. The one-step features are (x, y) coordinates scaled to [-1, 1], similar to Fig. 3. In the POMDP gridworld, the actions are stochastic, and the agent only sees a noisy indicator of state. In all domains, the discount is γ = 0.9. ... We evaluate directions mi that we optimized for during backups, as well as new random directions. ... this persistent error is due to our limited-size representation of Φ. The error decreases as we increase the number of boundary points that we store. |