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Research map / Reinforcement learning / Policy gradient and actor-critic

Self-Imitation Learning

ICML 2018 · Reinforcement learning › Policy gradient and actor-critic · approach: Actor-critic theory · 140 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add self-imitation learning to A2CA2C · 49 Atari gamesmedian human-normalized score: 96.1% -> 138.7%Helped
Add self-imitation learning to A2CA2C · Hard-exploration Atari gamesscoreHelped
Use A2C+SIL instead of A2CA2C · Venturescore: 0 -> 0No clear effect
Add self-imitation learning to A2CA2C · Key-Door-Treasurelearning speedHelped
Combine self-imitation learning with count-based explorationA2C · Apple-Key-Door-Treasureoptimal policy learningHelped
Add prioritized experience replay with ACER objectiveA2C · 49 Atari gamesmedian human-normalized score: 96.1% -> 46.8%Hurt
Add self-imitation learning to PPOPPO · MuJoCo: Swimmer, Walker2d, and AntrewardHelped
Add self-imitation learning to PPOPPO · Delayed-reward MuJoCo tasksrewardHelped
Reduce the number of SIL updates per iteration or lower the SIL loss weight later in training (ablation)A2C+SIL · James Bond and Star GunnerperformanceHelped

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