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..
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Authors: Harley E Wiltzer, David Meger, Marc G. Bellemare
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of such an algorithm in a synthetic control problem. We simulate the performance of the FD-WGF Q-learning algorithm on a simple task based on a continuous MDP suggested by Munos (2004) as an example of an MDP whose value function does not satisfy the HJB equation in the usual sense. |
| Researcher Affiliation | Collaboration | 1Mc Gill University, Montreal, Canada 2Mila Quebec AI Institute 3Google Brain, Montreal, Canada 4CIFAR Fellow. |
| Pseudocode | Yes | Algorithm 1 Continuous-time distributional RL update |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology, nor does it provide a link to a repository. |
| Open Datasets | Yes | We simulate the performance of the FD-WGF Q-learning algorithm on a simple task based on a continuous MDP suggested by Munos (2004) as an example of an MDP whose value function does not satisfy the HJB equation in the usual sense. |
| Dataset Splits | No | The paper does not specify exact percentages, sample counts, or refer to standard predefined splits for training, validation, and test datasets. |
| Hardware Specification | No | The paper does not provide specific details such as CPU/GPU models, memory, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions used for the implementation. |
| Experiment Setup | Yes | The discount factor is γ = 0.3, and observations occur at a frequency ω = 1k Hz. ... Algorithm 1 Continuous-time distributional RL update ... Require: WGF time parameter τ Require: Learning rate α |