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..
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Authors: Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present an empirical evaluation of both UCRL2 and EULER, and compare their performance to the proposed variants, which use greedy policy updates, UCRL2-GP and EULER-GP, respectively. The simulation results can be found in Figure 1, and clearly indicate that using greedy planning leads to negligible degradation in the performance. |
| Researcher Affiliation | Collaboration | Yonathan Efroni Technion, Israel Nadav Merlis Technion, Israel Mohammad Ghavamzadeh Facebook AI Research Shie Mannor Technion, Israel |
| Pseudocode | Yes | Algorithm 1 Real-Time Dynamic Programming, Algorithm 2 Model-based RL with Greedy Policies, Algorithm 3 UCRL2 with Greedy Policies (UCRL2-GP) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated the algorithms on two environments. (i) Chain environment [Osband and Van Roy, 2017]: In this MDP, there are N states, which are connected in a chain... (ii) 2D chain: A generalization of the chain environment... |
| Dataset Splits | No | The paper describes simulation environments and averages results over random seeds, but it does not specify explicit training/validation/test dataset splits as commonly found in supervised learning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper describes the environment parameters (e.g., N states, H horizon) and mentions averaging over 5 random seeds, but it does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, epochs, optimizer settings) for the algorithms tested. |