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..
Provably Efficient Maximum Entropy Exploration
Authors: Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a proof of concept, we implement the proposed method and demonstrate experiments over several mainstream RL tasks in Section 5. Section 5. Proof of Concept Experiments. We report the results from a preliminary set of experiments. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Princeton University 2Google AI Princeton 3Allen School of Computer Science and Engineering, University of Washington 4Department of Statistics, University of Washington. |
| Pseudocode | Yes | Algorithm 1 Maximum-entropy policy computation. Algorithm 2 Sample-based planning for an unknown MDP. Algorithm 3 Sample-based estimate of the state distribution. |
| Open Source Code | Yes | The open-source implementations may be found at https: //github.com/abbyvansoest/maxent. |
| Open Datasets | Yes | Pendulum. The 2-dimensional state space for Pendulum (from (Brockman et al., 2016)) was discretized evenly to a grid of dimension 8 8. Ant. The 29-dimensional state space for Ant (with a Mujoco engine). Humanoid. The 376-dimensional state space for the Mujoco Humanoid environment. |
| Dataset Splits | No | The paper describes how training was conducted (e.g., 'trained on 200 episodes'), but does not specify explicit train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn (Pedregosa et al., 2011)' but does not provide a specific version number for scikit-learn or any other software dependency. |
| Experiment Setup | Yes | Reward functional. Each planning agent was trained to maximize a smooth variant of the KL divergence objective. The smoothing parameter was chosen to be σ = |S|−1. Pendulum. The planning oracle is a REINFORCE (Sutton et al., 2000) agent, where the the output policy from the previous iteration is used as the initial policy for the next iteration. The policy class is a neural net with a single hidden layer consisting of 128 units. The agent is trained on 200 episodes every epoch. Ant. The planning oracle is a Soft Actor-Critic (Haarnoja et al., 2018) agent. The policy class is a neural net with 2 hidden layers composed of 300 units and the Re LU activation function. The agent is trained for 30 episodes, each of which consists of a roll-out of 5000 steps. The mixed policy is executed over 10 trials of T = 10000 steps at the end of each epoch. |