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..
Meta-trained agents implement Bayes-optimal agents
Authors: Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro Ortega
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically investigate this claim on a number of prediction and bandit tasks. [...] Thus, our main contribution is the investigation of the computational structure of RNN-based metalearned solutions. Specifically, we compare the computations of meta-learned agents against the computations of Bayes-optimal agents in terms of their behaviour and internal representations on a set of prediction and reinforcement learning tasks with known optimal solutions. |
| Researcher Affiliation | Industry | Vladimir Mikulik , Grégoire Delétang , Tom Mc Grath , Tim Genewein , Miljan Martic, Shane Legg, Pedro A. Ortega Deep Mind London, UK |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its code. |
| Open Datasets | No | The paper defines custom tasks using specific probability distributions (e.g., Bernoulli, categorical, exponential, Gaussian) from which data is generated, rather than using a pre-existing, publicly available dataset with a specific name and source. |
| Dataset Splits | No | The paper does not specify distinct training, validation, and test splits in a way that would allow direct reproduction of data partitioning for a fixed dataset. It mentions evaluating 'across many checkpoints of a training run' but does not define a dedicated validation set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like BPTT, Adam, and Impala but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We selected4 N = 32 for prediction tasks and N = 256 for bandit tasks. Networks were trained with BPTT [23, 24] and Adam [38]. In prediction tasks the loss function is the log-loss of the prediction. In bandit tasks the agents were trained to maximise the return (i.e., the discounted cumulative reward) using the Impala [39] policy gradient algorithm. |