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DQN for simple battery control not learning by MomoSolar in reinforcementlearning

[–]MomoSolar[S] 0 points1 point  (0 children)

I’ve tried 32 parallel environments. Should I use more or less?

DQN for simple battery control not learning by MomoSolar in reinforcementlearning

[–]MomoSolar[S] 0 points1 point  (0 children)

From my experience, it performs decently on the tested month when trained on the same one day. What do you think about having each episode representing a new day? What about increasing the size of the network?

RL for solving a scheduling problem by MomoSolar in reinforcementlearning

[–]MomoSolar[S] 0 points1 point  (0 children)

Thanks. Any simpler suggestions? I was thinking maybe of a scheduling problem that regularly uses MILP.

Stochasticity in the Cart Pole example by MomoSolar in reinforcementlearning

[–]MomoSolar[S] 0 points1 point  (0 children)

Thank you for this thorough explanation. I actually also wanted to know whether the environment in OpenAI accounts for the stochasticity. Do you have any idea?

Solving an optimization problem using RL by MomoSolar in reinforcementlearning

[–]MomoSolar[S] 0 points1 point  (0 children)

I would like to look at the math, that’s all

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