Questions tagged [experience-replay]

For questions related to the "experience replay" buffer (as used in the Deep Q Network and similar works).

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10
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3answers
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Why exactly do neural networks require i.i.d. data?

In reinforcement learning, successive states (actions and rewards) can be correlated. An experience replay buffer was used, in the DQN architecture, to avoid training the neural network (NN), which ...
15
votes
1answer
14k views

How does LSTM in deep reinforcement learning differ from experience replay?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are: How does this method differ from the ...
8
votes
2answers
549 views

What is experience replay in laymen's terms?

I've been reading Google's DeepMind Atari paper and I'm trying to understand the concept of "experience replay". Experience replay comes up in a lot of other reinforcement learning papers (...
3
votes
1answer
127 views

How to handle the final state in experience replay?

I'm using the DQN algorithm to train my agent to play a turn-based game. The memory replay buffer stores tuples of experiences $(s, a, r, s')$, where $s$ and $s'$ are consecutive states. At the last ...
2
votes
1answer
59 views

Could we update the policy network with previous trajectories using supervised learning?

I believe to understand the reason why on-policy methods cannot reuse trajectories collected from earlier policies: the trajectory distribution change with the policy and the policy gradient is ...
4
votes
1answer
239 views

How does being on-policy prevent us from using the replay buffer with the policy gradients?

One of the approaches to improving the stability of the Policy Gradient family of methods is to use multiple environments in parallel. The reason behind this is the fundamental problem we discussed in ...
1
vote
0answers
79 views

What is the advantage of using experience replay (as opposed to feeding it sequential data)?

Let's suppose that our RL agent needs to play a game with different levels. If we train our RL agent sequentially or with sequential data, our agent will learn how to play level 1, but then it will ...