AMPER (Adaptive Memory-Augmented Prioritized Experience Replay) is a reinforcement learning framework where agents dynamically adjust experience replay buffer size and prioritization, employing memory-augmented neural networks to enhance learning efficiency and adaptability in non-stationary environments requiring long-term memory.
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AMPER (Adaptive Memory-Augmented Prioritized Experience Replay) is a reinforcement learning framework where agents dynamically adjust experience replay buffer size and prioritization, employing memory-augmented neural networks to enhance learning efficiency and adaptability in non-stationary environments requiring long-term memory.
SilentDrift/AMPER
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AMPER (Adaptive Memory-Augmented Prioritized Experience Replay) is a reinforcement learning framework where agents dynamically adjust experience replay buffer size and prioritization, employing memory-augmented neural networks to enhance learning efficiency and adaptability in non-stationary environments requiring long-term memory.
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