  This paper proposes a cascading failure mitigation strategy based on
Reinforcement Learning (RL) method. Firstly, the principles of RL are
introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented
and its challenges are investigated. The problem is then tackled by the RL
based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL
framework (rewards, states, etc.) are also discussed in detail. Experiments on
the IEEE 118-bus system by both shallow and deep neural networks demonstrate
promising results in terms of reduced system collapse rates.
