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Policy Transfer with Strategy Optimization

ICLR 2019 · Reinforcement learning › Visual and representation RL · approach: Skill learning and diffusion policies · 56 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
search the target environment for the best strategy using CMA-ES rather than relying on a robust or adaptive policypolicy transfer · Hopper DART-to-MuJoCo, dim(μ)=5 or 10task performanceHelped
increase randomized dynamics from dim(μ)=2 to dim(μ)=5 (ablation)SO-CMA · Hopper DART-to-MuJoCotask performanceHelped
increase randomized dynamics from dim(μ)=5 to dim(μ)=10 (ablation)SO-CMA · Hopper DART-to-MuJoCotask performanceNo clear effect
use strategy optimization with CMA-ES instead of Hist or UPOSIpolicy transfer · Walker2d DART-to-MuJoCo with latencytask performanceHelped
use strategy optimization with CMA-ES instead of Robust or Histpolicy transfer · HalfCheetah DART-to-MuJoCo with delaytask performanceHelped
search for a strategy with CMA-ES rather than use the robust policyMinitaur actuator-model transfer · quadruped inaccurate-to-accurate actuator modeltask performanceHelped
search for a strategy with CMA-ES rather than use baseline policiespolicy transfer · Hopper rigid-to-deformable-foot transfertask performanceHelped
use Bayesian optimization instead of CMA-ES for strategy search (ablation)strategy optimization · Hopper and Quadrupedtask performanceNo clear effect
use feed-forward neural networks rather than an LSTM to learn the dynamics model (ablation)model-based strategy optimization · Hopper DART-to-MuJoCotask performanceHurt
use an LSTM model-based optimizer instead of CMA-ES (ablation)strategy optimization · Hopper DART-to-MuJoCotask performanceNo clear effect
train with low-dimensional randomized dynamics rather than a higher-dimensional family of dynamics (ablation)policy transfer methods · Hopper DART-to-MuJoCo with different ankle joint limitstask performanceHurt

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