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Research map / Reinforcement learning / Visual and representation RL

Flexible Option Learning

NeurIPS 2021 · Reinforcement learning › Visual and representation RL · approach: Probabilistic and autonomous RL · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Update all relevant options instead of only the sampled optionOption-critic in FourRooms with fixed hallway options · FourRoomssteps per episodeHelped
Update all relevant optionsOption-critic, compared with OC · FourRoomsepisodes to reach the same performanceHelped
Update all relevant optionsOption-critic, compared with OC · MountainCar transferperformance and sample efficiencyHelped
Increase the probability of updating all options (η) (ablation)MOC · MountainCar transferfinal performanceHelped
Update all options instead of only the sampled optionOption-critic, compared with IOC and PPO · MuJoCo transfer tasksperformance and sample efficiencyHelped
Use eight options rather than fewer options (ablation)MOC · Walker2dStand-v1performance: 617 (98) -> 535 (81)Hurt
Use eight options rather than fewer options (ablation)IOC · Walker2dStand-v1performance: 326 (92) -> 212 (64)Hurt
Set η=1.0 rather than using intermediate η (ablation)MOC · FourRoomsoption diversity (information radius)Hurt
Use intermediate η values instead of η=1.0 (ablation)MOC · FourRoomsoption diversity (information radius)Helped
Increase η (ablation)MOC · MountainCaroption diversity (information radius)No clear effect
Use MOC rather than IOCOption-critic with learned option policies · MuJoCo option durationoption durationHelped
Use MOC rather than IOCPPO-based hierarchical agents · MuJoCo with independent second-to-last hidden layersperformance robustnessHelped

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