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About this paper
Giacomo Spigler
Proximal Policy Distillation (PPD) integrates Proximal Policy Optimization (PPO) with a clipped KL-divergence distillation loss to enable student agents to learn from a teacher while leveraging environmental rewards. The method consistently achieves higher sample efficiency and allows student policies to surpass the performance of their teachers, even when learning from imperfect instructors.
Key insights
- Combining PPO's stable optimization with a principled, clipped distillation loss allows students to effectively integrate knowledge from teachers with their own environmental feedback.
- Student-driven exploration is crucial for distilling policies, enabling students to learn more robustly and generalize better than when relying solely on teacher-generated data.
- Applying proximal constraints (clipping) to both the PPO objective and the distillation loss ensures training stability and allows for more effective learning beyond strict imitation.
Summary by alphaXiv.
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