Proposes min-max IPR and percentile highlighting to evaluate run-to-run performance variation in deep RL, with case studies on normalizations in PPO/SAC, algorithm comparisons, and DQN/Rainbow on Atari.
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Performance Variation in Deep Reinforcement Learning
Proposes min-max IPR and percentile highlighting to evaluate run-to-run performance variation in deep RL, with case studies on normalizations in PPO/SAC, algorithm comparisons, and DQN/Rainbow on Atari.