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

Composing Entropic Policies using Divergence Correction

ICML 2019 · Reinforcement learning › Policy gradient and actor-critic · approach: Model-based RL with guarantees · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Correct the composed action value using the learned future policy-divergence termComposition of max-ent policies · Tabular 8x8 grid world, tricky tasklog regretHelped
Use compositional optimism, which combines component action values without a divergence correctionComposition of max-ent policies · Tabular 8x8 grid world, Left-Rightlog regretHurt
Use max-ent GPI to select actions from the best component-task valueComposition of max-ent policies · Tabular 8x8 grid world, Left-Uplog regretHurt
Use compositional optimism when the component policies are compatibleComposition of max-ent policies · Tabular 8x8 grid world, Left-Uplog regretHelped
Correct the composed action value using learned future divergenceComposition of max-ent policies · Point mass, tricky taskreturnsHelped
Correct the composed action value using learned future divergenceComposition of max-ent policies · Planar manipulator, tricky taskreturnsHelped
Correct the composed action value using learned future divergenceComposition of max-ent policies · Jumping ball, tricky taskreturnsHelped
Correct the composed action value using learned future divergenceComposition of max-ent policies · Ant, tricky taskreturnsHelped
Replace the task-conditional correction with a correction estimated from b=1/2 (ablation)Divergence Correction (DC) · Point mass, tricky taskreturnsHurt
Add a max-ent GPI lower-bound safeguard to DC-Cheap (ablation)DC-Cheap · Point mass, tricky taskreturnsHelped
Replace the task-conditional correction with a correction estimated from b=1/2 (ablation)Divergence Correction (DC) · Planar manipulator, tricky taskreturnsNo clear effect
Replace the task-conditional correction with a correction estimated from b=1/2 (ablation)Divergence Correction (DC) · Ant, tricky taskreturnsNo clear effect

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