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..
Control Regularization for Reduced Variance Reinforcement Learning
Authors: Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach empirically on a range of settings, and demonstrate significantly reduced variance, guaranteed dynamic stability, and more efficient learning than deep RL alone. |
| Researcher Affiliation | Academia | 1California Institute of Technology, Pasadena, CA 2Rice University, Houston, TX 3University of Michigan, Ann Arbor, MI. |
| Pseudocode | Yes | Algorithm 1 Control Regularized RL (CORE-RL) |
| Open Source Code | Yes | All code can be found at https://github.com/rcheng805/CORE-RL. |
| Open Datasets | Yes | We apply the CORE-RL algorithm to control of the cartpole from the Open AI gym environment (Cart Pole-v1). ... The experimental setup and data collection process are described in (Ge et al., 2018). |
| Dataset Splits | No | The paper describes running experiments multiple times with different random seeds and splitting data into episodes, but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit splits for the environments). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like DDPG, PPO, TRPO, Open AI gym, and TORCS, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For all three problems, we use DDPG as the policy gradient RL algorithm (Lillicrap et al., 2016). We use a neural network with 2 hidden layers with 64 neurons in each layer. We use the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 0.001. We use a batch size of 64, and discount factor of 0.99. We use a replay buffer with size of 106. We found that the Adaptive Mixing Strategy performs best when λmax = 50, and C = 0.0005. |