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..
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Authors: Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments and Results. For each of these domains, we compare our formulation of the Hi P-MDP with embedded latent parameters (equation 2) with four baselines (one model-free and three model-based) to demonstrate the efficiency of learning a policy for a new instance b using the Hi P-MDP. These comparisons are made across the first handful of episodes encountered in a new task instance to highlight the advantage provided by transferring information through the Hi P-MDP. The Hi P-MDP with embedded wb outperforms all four benchmarks. |
| Researcher Affiliation | Collaboration | Taylor Killian EMAIL Harvard University Samuel Daulton EMAIL Harvard University, Facebook George Konidaris EMAIL Brown University Finale Doshi-Velez EMAIL Harvard University |
| Pseudocode | Yes | Algorithm 1 Learning a control policy w/ the Hi P-MDP |
| Open Source Code | Yes | Example code for training and evaluating a Hi P-MDP, including the simulators used in this section, can be found at http://github.com/dtak/hip-mdp-public. |
| Open Datasets | Yes | We revisit the 2D demonstration problem from Section 3, as well as describe results on both the acrobot [42] and a more complex healthcare domain: prescribing effective HIV treatments [15] to patients with varying physiologies. (Acrobot [42] refers to "R Sutton and A Barto. Reinforcement learning: an introduction, volume 1. MIT Press, Cambridge, 1998." and HIV [15] refers to "D Ernst, G Stan, J Goncalves, and L Wehenkel. Clinical data based optimal STI strategies for HIV: a reinforcement learning approach. In Proceedings of the 45th IEEE Conference on Decision and Control, 2006." both are standard academic benchmarks/problems). |
| Dataset Splits | No | The paper describes the training process (e.g., "trained on observations from a single episode", use of "global replay buffer D" and "instance-specific replay buffer Db") and update procedures, but it does not specify explicit numerical percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details, such as CPU models, GPU types, or memory specifications, used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms like Bayesian Neural Networks (BNNs), Adam optimizer, and Double Deep Q Networks (DDQNs), but it does not provide specific version numbers for these or any other software libraries or programming languages used. |
| Experiment Setup | Yes | Specific modeling details such as number of epochs, learning rates, etc. are described in Appendix C. |