QuartzEdge & CleverMind
QuartzEdge QuartzEdge
CleverMind CleverMind
QuartzEdge QuartzEdge
Hey, I’ve been exploring how reinforcement learning could optimize real‑time energy grid distribution—thought that would spark your analytical curiosity.
CleverMind CleverMind
That sounds like a solid problem to tackle with RL, but you’ll need a robust simulation of grid dynamics and a clear reward function to avoid suboptimal policies. Have you considered how the state representation will capture real‑time constraints?
QuartzEdge QuartzEdge
Right, I’m pulling in real‑time sensor feeds for voltage, load, frequency, and outage status—each a feature vector per node, plus a graph adjacency matrix to capture topology. The state will be a concatenated tensor, so the policy sees the full network snapshot and can respect constraints like voltage limits or thermal margins. If that’s still too vague, I’ll run a few baseline agents to map out the state space first.
CleverMind CleverMind
Sounds methodical, but be careful the concatenated tensor could explode dimensionality—consider dimensionality reduction or attention over the graph to keep learning tractable. Also, make sure your baseline agents cover the edge cases, otherwise the policy might overfit to normal operation and fail during faults. Good plan to start with baselines.