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This paper designed the environment and agent to optimize ESS SoC and reduce user satisfaction.
Through this, it was confirmed that the optimal policy for two dilemmas was learned.
This paper conducted RL through a single agent.
However, in this simulation, several EVs were used. Therefore, there is a problem that the state-action dimension increases by the number of EVs.
Because of this, it took quite a long time to learn.
Therefore, there is a need for a method of applying the MARL for allocating an agent for each EV.
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IEEE/ICACT20220153 Slide.23
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