Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarboniz-ing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a re-sult, power system tends to have less backup capacity and operate near their designed limit, thus exacerbating system vulnerability against extreme events. Under this reality, resilient control for the multi-energy micro-grid is facing the following challenges, which are: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the effect from the stochastic uncertainties of</i> RES; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the need for a model-free and fast-reacting control scheme under extreme events; and</i> 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient exploration and robust performance with limited extreme events data</i> . <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">To deal with the aforementioned challenges, this paper pro-poses a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In partic-ular, the proposed approach replaces the deterministic network in traditional Reinforcement Learning with a Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves the Q-value overestimation issue. Compared with the naive Deep Deterministic Policy Gra-dient (DDPG) method and optimization method, the effectiveness and importance of employing the Bayesian Reinforcement Learn-ing approach are investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed Bayesian Deep Deterministic Policy Gradient (BDDPG) method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach.