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How is replay memory used in a DQN?
Now, before we can move on to discussing exactly how a DQN is trained, we’re first going to explain the concepts of experience replay and replay memory. With deep Q-networks, we often utilize this technique called experience replay during training.
How are experiences stored in the replay memory?
All of the agent’s experiences at each time step over all episodes played by the agent are stored in the replay memory. Well actually, in practice, we’ll usually see the replay memory set to some finite size limit, N, and therefore, it will only store the last N experiences.
Is there a size limit to replay memory?
Well actually, in practice, we’ll usually see the replay memory set to some finite size limit, N, and therefore, it will only store the last N experiences. This replay memory data set is what we’ll randomly sample from to train the network.
How is memory replay used in RL algorithms?
In standard RL algorithms, an experience is immediately discarded after it’s used for an update. Re- centbreakthroughsinRLleveragedanimportanttechnique called experience replay (ER), in which experiences are stored in a memory buffer of certain size; when the buffer
What’s the difference between DQN and experience replay?
The Q-Learning targets when using experience replay use the same targets as the online version, so there is no new formula for that. The loss formula given is also the one you would use for DQN without experience replay. The difference is only which s, a, r, s’, a’ you feed into it.
What happens if you take random samples from replay memory?
If the network learned only from consecutive samples of experience as they occurred sequentially in the environment, the samples would be highly correlated and would therefore lead to inefficient learning. Taking random samples from replay memory breaks this correlation.
Why is replay memory used in deep learning?
A key reason for using replay memory is to break the correlation between consecutive samples. If the network learned only from consecutive samples of experience as they occurred sequentially in the environment, the samples would be highly correlated and would therefore lead to inefficient learning.