An action optimization device for optimizing an action for controlling air conditioning in a target space, comprising a processor and a memory connected to the processor, the device comprising: an environmental data acquisition part configured to acquire environmental data related to a state of the environment in the target space including a people flow, a temperature, and a humidity and to store the acquired environmental data in an environmental data storage unit; an environmental data interpolation part configured to perform time/space interpolation on the acquired environmental data according to a preset algorithm; an environment reproduction model training part configured to train an environment reproduction model, based on the time/space-interpolated environmental data, such that, when a state s1 of an environment and an action a1 for controlling the environment are input, a correct answer value of an environmental state s1' after the action a1 is output; an exploration model training part configured to train an exploration model such that an action a2 to be taken next is output when an environmental state s2 output from the environment reproduction model is input, and store the trained exploration model in the memory; an exploration part configured to explore for a first action to be taken for the first environmental state by using the trained exploration model; characterized by an environment reproduction part configured to predict a second environment state corresponding to the first environment state and the first action by using the trained environment reproduction model; and an output interface unit configured to output a result of the exploration, wherein the environment reproduction part is further configured to input the first environment state, the second environment state, and the first action to a preset reward function and to output a reward value, and wherein the exploration model training part is further configured to performs training of the exploration model by updating a training result of the exploration model based on the reward value output from the environment reproduction part . an environmental data acquisition part configured to acquire environmental data related to a state of the environment in the target space including a people flow, a temperature, and a humidity and to store the acquired environmental data in an environmental data storage unit; an environmental data interpolation part configured to perform time/space interpolation on the acquired environmental data according to a preset algorithm; an environment reproduction model training part configured to train an environment reproduction model, based on the time/space-interpolated environmental data, such that, when a state s1 of an environment and an action a1 for controlling the environment are input, a correct answer value of an environmental state s1' after the action a1 is output; an exploration model training part configured to train an exploration model such that an action a2 to be taken next is output when an environmental state s2 output from the environment reproduction model is input, and store the trained exploration model in the memory; an exploration part configured to explore for a first action to be taken for the first environmental state by using the trained exploration model; an environment reproduction part configured to predict a second environment state corresponding to the first environment state and the first action by using the trained environment reproduction model; and an output interface unit configured to output a result of the exploration, wherein the environment reproduction part is further configured to input the first environment state, the second environment state, and the first action to a preset reward function and to output a reward value, and wherein the exploration model training part is further configured to performs training of the exploration model by <EOS>