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Generalized Proximal Policy Optimization with Sample Reuse

NeurIPS 2021 · Reinforcement learning › Policy gradient and actor-critic · approach: Model-based RL with guarantees · 21 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · Swimmer-v3average performance over 1M steps: 98 -> 161Helped
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · Hopper-v3average performance over 1M steps: 2{,}362 -> 2{,}544Helped
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · HalfCheetah-v3average performance over 1M steps: 1{,}764 -> 2{,}439Helped
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · Walker2d-v3average performance over 1M steps: 1{,}817 -> 2{,}199Helped
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · Ant-v3average performance over 1M steps: 545 -> 762Helped
Reuse samples from recent policies with generalized clipping and adaptive learning rate (GePPO instead of PPO)PPO · Humanoid-v3average performance over 1M steps: 584 -> 665Helped
Use GePPO instead of PPOPPO · Swimmer-v3steps (M) to final PPO performance: 1.00 -> 0.23Helped
Use GePPO instead of PPOPPO · Hopper-v3steps (M) to final PPO performance: 1.00 -> 0.41Helped
Use GePPO instead of PPOPPO · HalfCheetah-v3steps (M) to final PPO performance: 1.00 -> 0.54Helped
Use GePPO instead of PPOPPO · Walker2d-v3steps (M) to final PPO performance: 1.00 -> 0.63Helped
Use GePPO instead of PPOPPO · Ant-v3steps (M) to final PPO performance: 1.00 -> 0.79Helped
Use GePPO instead of PPOPPO · Humanoid-v3steps (M) to final PPO performance: 1.00 -> 0.85Helped

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