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..
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Authors: Luis Haug, Sebastian Tschiatschek, Adish Singla
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments Our experimental setup is similar to the one in [Abbeel and Ng, 2004], i.e., we use N N gridworlds in which non-overlapping square regions of neighbouring cells are grouped together to form n n macrocells for some n dividing N. ... The plots in Figure 3 illustrate the significance of the teaching risk for the problem of teaching a learner under worldview mismatch. ... We compared the performance of TRGREEDY (Algorithm 1) to two variants of the algorithm... |
| Researcher Affiliation | Collaboration | Luis Haug Department of Computer Science ETH Zurich EMAIL Sebastian Tschiatschek Microsoft Research Cambridge, UK EMAIL Adish Singla Max Planck Institute for Software Systems Saarbrücken, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 TRGREEDY: Featureand demo-based teaching with TR-greedy feature selection Require: Reward vector w , set of teachable features F, feature budget B, initial worldview AL, teacher policy πT , initial learner policy πL, performance threshold ε. for i = 1, . . . , B do if | w , µ(πL) w , µ(πT ) | > ε then f arg minf F ρ(AL f, ; w ) T selects feature to teach AL AL f, L s worldview gets updated πL LEARNING(πL, ALµ(πT )) L trains a new policy else return πL end if end for return πL |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes using 'N N gridworlds' and sampling 'reward weights w Rk randomly', indicating a synthetic environment setup rather than a specific publicly available dataset with concrete access information. While it references [Abbeel and Ng, 2004] for the setup type, it does not provide access information for a dataset. |
| Dataset Splits | No | The paper discusses training and learning rounds, but it does not provide specific details on dataset splits such as explicit training, validation, and test sets with percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'the projection version of the apprenticeship learning algorithm from [Abbeel and Ng, 2004]' but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | To obtain these plots, we used a gridworld with N = 20, n = 2; for each value ℓ [1, 100], we sampled five random worldview matrices AL Rℓ 100, and let L train a policy πL using the projection algorithm in [Abbeel and Ng, 2004]... The discount factor used was γ = 0.9 in all cases. |