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Disentangling Transfer in Continual Reinforcement Learning

NeurIPS 2022 · Continual learning › Online and replay-based · approach: Plasticity-preserving continual reinforcement learning · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
carry over the critic from the previous task (ablation)SAC · CW10 task pairsforward transfer: 0.20Helped
carry over the actor from the previous task (ablation)SAC · CW10 task pairsforward transfer: 0.06Helped
carry over the exploration policy (ablation)SAC · CW10 task pairsforward transfer: 0.09Helped
freeze critic body and train only head (ablation)SAC critic · CW10 task pairsforward transfer: -1.29Hurt
carry over actor, critic and exploration together vs separately (ablation)SAC · CW10 task pairsforward transfer: 0.35Helped
carry over the optimizer statistics (ablation)SAC · CW10 task pairsforward transfer: 0.02No clear effect
longer exploration phase (50000 steps vs 5000) when carrying over exploration only (ablation)SAC · CW10 task pairsforward transfer: 0.13 -> -0.00Hurt
add uniform-previous exploration to EWC (ablation)EWC · CW10average performance: 0.63 -> 0.72Helped
add best-return exploration to EWC (ablation)EWC · CW20forward transfer: -0.14 -> 0.28Helped
add informed exploration strategies to PackNet (ablation)PackNet · CW10forward transfer: 0.26 -> 0.23No clear effect
add behavioral cloning of the actor vs Perfect memory/BC of actor and critic in two-task transfer (ablation)SAC · CW10 task pairsforward transfer: 0.01; -0.56Mixed
replace Perfect memory with actor behavioral cloning on long sequences (ablation)SAC · CW10forward transfer: -1.13 -> 0.41Helped

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