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..
Discovering a set of policies for the worst case reward
Authors: Tom Zahavy, Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our algorithm on a grid world and also on a set of domains from the Deep Mind control suite. We confirm our theoretical results regarding the monotonically improving performance of our algorithm. |
| Researcher Affiliation | Industry | Tom Zahavy , Andre Barreto, Daniel J Mankowitz, Shaobo Hou, Brendan O Donoghue, Iurii Kemaev and Satinder Singh Deep Mind |
| Pseudocode | Yes | Algorithm 1 SMP worst case policy iteration |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the described methodology or a direct link to a source-code repository. |
| Open Datasets | Yes | We empirically evaluate our algorithm on a grid world and also on a set of domains from the Deep Mind control suite. Next, we conducted a set of experiments in the DM Control Suite (Tassa et al., 2018). |
| Dataset Splits | No | The paper describes training policies and evaluating them, but it does not specify explicit dataset splits (e.g., percentages or counts) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions specific software components like "STACX (Zahavy et al., 2020d)" and "CVXPY (Diamond & Boyd, 2016)" but does not provide version numbers for them. |
| Experiment Setup | Yes | At each iteration (x-axis) of Algorithm 1 we train a policy for 5 105 steps to maximize w SMP Πt . We then compute the SFs of that policy using additional 5 105 steps and evaluate it w.r.t w SMP Πt . At each iteration of Algorithm 1 we train a policy for 2 106 steps using an actor-critic (and specifically STACX (Zahavy et al., 2020d)) to maximize w SMP Πt , add it to the set, and compute a new w SMP Πt+1. We focused on the setup where the agent is learning from feature observations corresponding to the positions and velocities of the body in the task (pixels were only used for visualization). |