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Imitating Latent Policies from Observation

ICML 2019 · Reinforcement learning › Imitation learning · approach: Offline imitation from imperfect data · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Learn a latent policy from expert state observations, then align latent actions with real actions using a small number of environment interactionsILPO · Cartpoleimitation rewardHelped
Learn a latent policy from expert state observations, then align latent actions with real actions using a small number of environment interactionsILPO · Acrobotimitation rewardHelped
Learn a latent policy from expert state observations, then align latent actions with real actions using a small number of environment interactionsILPO · Mountain carimitation rewardHelped
Learn a latent policy from expert state observations, then align latent actions with real actions using a small number of environment interactionsILPO · CoinRun easyimitation rewardHelped
Learn a latent policy from expert state observations, then align latent actions with real actions using a small number of environment interactionsILPO · CoinRun hardimitation rewardHelped
Use one latent action (|Z|=1) (ablation)ILPO · Cartpole and Acrobotimitation rewardHurt
Use deterministic rather than stochastic demonstrations (ablation)ILPO · Cartpole and Acrobotimitation rewardHurt

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