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Record W7124298994 · doi:10.65109/nind4053

Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning

2023· article· W7124298994 on OpenAlexaff
Chaitanya Kharyal, Tanmay Kumar Sinha, Sai Krishna Gottipati, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicReinforcement Learning in Robotics
Canadian institutionsUniversity of AlbertaInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsReinforcement learningAutomationRobotState (computer science)Task (project management)RoboticsRobot learningTrain

Abstract

fetched live from OpenAlex

The future of industrial automation is hinged on the ability of the industrial robots to precisely finish the tasks designated for them [5]. These tasks are usually specified in terms of a state the robot is required to reach (i.e., a goal state). Goal-conditioned reinforcement learning [7, 8] is an emerging sub-field that trains policies with goal inputs. This enables the agent to generalize to new unseen goals, learn multiple complex tasks and acquire new skills along the way.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.288
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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