One of the basic elements for increasing the stability of Q-function learning is the use of an experience replay buffer. Increasing the buffer makes it possible to collect more diverse examples of interaction with the environment. This allows our model to better study and reproduce the Q-function of the environment. This technique is widely used in various reinforcement learning algorithms, including algorithms of the Actor-Critic family. more...
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