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In order to have anything resembling reinforcement learning you must at the very least have a set of states $S$ and a set of actions $A$. In your formulation I can vaguely identify the set of states $S$ as all possible $(x,y,z)$ triplets. But don't see anything in your description that could be interpreted as a set of actions $A$. You either oversimplified ...


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I found out the problem why the optimisation process got stuck and never moved closer to global optimum. It's because of the rate between 'explore' or 'exploit'. Basically, in RL, agent 'explore's by doing a random action and to find new solutions, 'exploit's the existing so-called known max future rewards to do the max action. Initially, I put the agent to ...


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