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..
Assumed Density Filtering Q-learning
Authors: Heejin Jeong, Clark Zhang, George J. Pappas, Daniel D. Lee
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate that ADFQ outperforms comparable algorithms on various Atari 2600 games, with drastic improvements in highly stochastic domains or domains with a large action space. |
| Researcher Affiliation | Academia | 1University of Pennsylvania, Philadelphia, PA 19104 2Cornell Tech, New York, NY 10044 |
| Pseudocode | Yes | Algorithm 1 ADFQ algorithm |
| Open Source Code | Yes | Example source code is available online1. 1https://github.com/coco66/ADFQ |
| Open Datasets | Yes | We tested on six Atari games, Enduro (|A| = 9), Boxing (|A| = 18), Pong (|A| = 6), Asterix (|A| = 9), Kung-Fu Master (|A| = 14), and Breakout (|A| = 4), from the Open AI gym simulator [Brockman et al., 2016]. |
| Dataset Splits | No | Each learning was greedily evaluated at every epoch (= TH/100) for 3 times, and their averaged results are presented in Fig.5. The entire experiment was repeated for 3 random seeds. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running experiments were provided in the paper. |
| Software Dependencies | No | For baselines, we used DQN and Double DQN with prioritized experience replay implemented in Open AI baselines2. |
| Experiment Setup | Yes | We used prioritized experience replay [Schaul et al., 2015] and a combined Huber loss functions of mean and variance. (...) We used ϵ-greedy action policy with ϵ annealed from 1.0 to 0.01 for the baselines as well as ADFQ. (...) Rewards were normalized to { 1, 0, 1} and different from raw scores of the games. |