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nbro
nbro
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Why experience reply memory in DQN instead of a rnnRNN memory?

I was trying to implement a DQN without experience reply memory, and the agent is not learning anything at all. I know from readings;readings that experience reply isis used for stabilizing gradients. But how important is experience reply in DQN and similar rlRL algorithms.? If the model needs to learn from memory, why dontdon't we use a recurrent network, which has inbuilt memory to it? What is the advantage of experience reply over a recurrent memory?

Why experience reply memory in DQN instead of a rnn memory?

I was trying to implement a DQN without experience reply memory and the agent is not learning anything at all. I know from readings; experience reply is used for stabilizing gradients. But how important is experience reply in DQN and similar rl algorithms. If the model needs to learn from memory why dont we use a recurrent network which has inbuilt memory to it? What is the advantage of experience reply over a recurrent memory?

Why experience reply memory in DQN instead of a RNN memory?

I was trying to implement a DQN without experience reply memory, and the agent is not learning anything at all. I know from readings that experience reply is used for stabilizing gradients. But how important is experience reply in DQN and similar RL algorithms? If the model needs to learn from memory, why don't we use a recurrent network, which has inbuilt memory to it? What is the advantage of experience reply over a recurrent memory?

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Eka
Eka
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Why experience reply memory in DQN instead of a rnn memory?

I was trying to implement a DQN without experience reply memory and the agent is not learning anything at all. I know from readings; experience reply is used for stabilizing gradients. But how important is experience reply in DQN and similar rl algorithms. If the model needs to learn from memory why dont we use a recurrent network which has inbuilt memory to it? What is the advantage of experience reply over a recurrent memory?