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Personally, I would choose the following two as the most important: epsilon: When using an epsilon-greedy policy, epsilon determines how often the agent should explore and how often it should exploit. Balancing exploration and exploitation is crucial for the success of the learning agent. Too little exploration might not teach anything to the agent and too ...


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You should read this study https://arxiv.org/abs/2006.05990 which does some empirical study on this question, specifically for on-policy, continuous action space DRL. It suggests that discount factor and learning rate are the two most important parameters to tune, followed by the width of the policy/value functions. That study also reports that it's very ...


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