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..
Learning Markov State Abstractions for Deep Reinforcement Learning
Authors: Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our approach on a visual gridworld domain and a set of continuous control benchmarks. Our approach learns representations that capture the underlying structure of the domain and lead to improved sample efficiency over state-of-the-art deep reinforcement learning with visual features often matching or exceeding the performance achieved with hand-designed compact state information. |
| Researcher Affiliation | Academia | Cameron Allen Brown University Neev Parikh Brown University Omer Gottesman Brown University George Konidaris Brown University |
| Pseudocode | No | The paper describes its methods in prose and with architectural diagrams (e.g., Figure 1) but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code repository available at https://github.com/camall3n/markov-state-abstractions. |
| Open Datasets | Yes | a collection of image-based, continuous control tasks from the Deep Mind Control Suite (Tassa et al., 2020). |
| Dataset Splits | No | The paper mentions training and evaluating models but does not provide specific details on validation dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU specifications, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not provide specific version numbers for software libraries, frameworks, or programming languages (e.g., PyTorch, Python, CUDA versions). |
| Experiment Setup | Yes | We use a total training batch size of 256 for all DeepMind Control tasks. We use the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 1e-4 for all trainable parameters. ... by minimizing LMarkov (with α = β = 1, η = 0). |