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End-to-end training of deep visuomotor policies

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it

fields

cs.LG 2

years

2020 1 2019 1

verdicts

UNVERDICTED 2

representative citing papers

Exploring Model-based Planning with Policy Networks

cs.LG · 2019-06-20 · unverdicted · novelty 7.0

POPLIN combines policy networks with model-predictive planning by optimizing either action sequences or policy parameters, yielding 3x better sample efficiency than PETS, TD3 and SAC on MuJoCo locomotion tasks.

citing papers explorer

Showing 2 of 2 citing papers.

  • Generative Language Modeling for Automated Theorem Proving cs.LG · 2020-09-07 · unverdicted · none · ref 17

    GPT-f, a transformer-based prover for Metamath, generated new short proofs that were accepted into the main library—the first such contribution from a deep-learning system.

  • Exploring Model-based Planning with Policy Networks cs.LG · 2019-06-20 · unverdicted · none · ref 22

    POPLIN combines policy networks with model-predictive planning by optimizing either action sequences or policy parameters, yielding 3x better sample efficiency than PETS, TD3 and SAC on MuJoCo locomotion tasks.