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..
Reward-Free Exploration for Reinforcement Learning
Authors: Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give an efficient algorithm that conducts O(S2Apoly(H)/ϵ2) episodes of exploration and returns ϵ-suboptimal policies for an arbitrary number of reward functions. We also give a nearly-matching Ω(S2AH2/ϵ2) lower bound, demonstrating the near-optimality of our algorithm in this setting. |
| Researcher Affiliation | Collaboration | 1Princeton University 2Microsoft Research, New York 3University of California, Berkeley 4Massachusetts Institute of Technology. |
| Pseudocode | Yes | Algorithm 2 Reward-free RL-Explore, Algorithm 3 Reward-free RL-Plan, Algorithm 4 Natural Policy Gradient (NPG) |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and theoretical bounds for reinforcement learning in MDPs. It does not describe experiments that involve training on a specific, publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or specific dataset splits (training, validation, test) needed for reproduction. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies or version numbers needed to replicate any experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameter values or training configurations. |