I am experimenting with OpenAI Gym and reinforcement learning. As far as I understood, the environment is waiting for the agent to make a decision, so it's a sequential operation like this:

decision = agent.decide(state)
state, reward, done = environment.act(decision)
agent.train(state, reward)

Doing it in this sequential way, the Markov property is fulfilled: the new state is a result of the old state and the action. However, a lot of games will not wait for the player to make a decision. The game will continue to run and perhaps, the action comes too late.

Has it been observed or is it even possible that a neuronal network adjusts its weights so that the PC computes the result faster and thus makes the "better" decision? E.g. one AI beats another because it is faster.

Before posting an answer like "there are always the same amount of calculations, so it's impossible", please be aware that there is caching (1st level cache versus RAM), branch prediction and maybe other stuff.


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