This paper develops deep reinforcement learning (DRL) algorithms for optimizing the operation of home energy system which consists of photovoltaic (PV) panels, battery ener‐ gy storage system, and household appliances. Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation. However, discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete ac‐ tions or continuous actions. Thus, a mixed deep reinforcement learning (MDRL) algorithm is proposed, which integrates deep Q-learning (DQL) algorithm and deep deterministic policy gra‐ dient (DDPG) algorithm. The DQL algorithm deals with dis‐ crete actions, while the DDPG algorithm handles continuous ac‐ tions. The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment. However, unsafe actions, which violate system constraints, can give rise to great cost. To handle such problem, a safe-MDRL algorithm is fur‐ ther proposed. Simulation studies demonstrate that the pro‐ posed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management. The proposed MDRL algorithm reduces the oper‐ ation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset. Moreover, the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.