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PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks

AAAI 2023 · Reinforcement learning › Visual and representation RL · approach: RL with tree search for discovery · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Combine PPO topology selection with threshold-based overload and recovery heuristicsPowRL · L2RPN NeurIPS 2020 Onlinescenario score: 61.48 vs 61.05Helped
Use PPO topology selection with threshold-based heuristics instead of rl_agentPowRL · L2RPN NeurIPS 2020 Offlinescenario score: 59.69 vs 61.06Hurt
Use PPO topology selection with threshold-based heuristics instead of binbinchenPowRL · L2RPN NeurIPS 2020 Offlinescenario score: 59.69 vs 3.96Helped
Use PPO topology selection with threshold-based heuristics instead of lujixiangPowRL · L2RPN NeurIPS 2020 Onlinescenario score: 61.48 vs 45Helped
Use PPO topology selection with threshold-based heuristics instead of expert_heuristicPowRL · L2RPN NeurIPS 2020 Onlinescenario score: 61.48 vs 22.53Helped
Use PPO topology selection with threshold-based heuristics instead of Do_NothingPowRL · L2RPN WCCI 2020scenario score: 96.4 vs 50.09Helped
Use PowRL instead of rl_agentPowRL · L2RPN WCCI 2020scenario score: 96.4 vs 96.4No clear effect
Restrict the topology action set to 240 selected actions rather than working with nearly 70k actionsPowRL · L2RPN challenge datasetsscenario scoreHelped
Use topology actions without costly generator redispatchPowRL · L2RPN NeurIPS 2020 Onlinefully survived scenarios: 16 scenariosNo clear effect

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