For questions about the double DQN model introduced in the paper "Deep Reinforcement Learning with Double Q-learning" (2015) by Hado van Hasselt et al.
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What exactly is the advantage of double DQN over DQN?How can I ensure convergence of DDQN, if the true Q-values for different actions in the same state are very close?
Finding the true Q-values in gymnaiusm
Why does regular Q-learning (and DQN) overestimate the Q values?
What does the notation $p_t = \text{max}_{i<t} p_i$ mean in algorithm 1 of the prioritized experience replay paper?
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