###### Abstract  
Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function \(H\) is then designed to help judge the goodness of the emotions. Based on \(H\), an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations. multi-agent collaboration; intrinsic motivation; MADDPG; emotion; deep reinforcement learning +
Footnote †: journal: Medical  
search and rescue, environmental detection, warehouse handling, target roundup, etc. [8]. In the above application scenarios, the single agent can no longer search the target well, so researchers gradually pay attention to multi-agent collaborative target search. A wrong strategy will reduce the efficiency of the agent search, so the cooperative strategy between multi-agents is the key of the multi-agent collaborative target search. [PERSON] et al. [9] introduced the Shapley function and fuzzy Shapley function to facilitate target search in a two-dimensional region with time constraints by minimizing the mission time and fuel usage. [PERSON] et al. [10] presented a method for analyzing the upper bound for time to find a target under the potential field guidance algorithm assuming a radially expanding search area. [PERSON] et al. [11] proposed an adaptive robotic bat algorithm (ARBA) for multi-robot target searching in unknown environments. The obstacle avoidance problem and the mechanism of jumping out of the local optimum are considered. The idea of a method of cooperative strategy between multi-agents is to transform it into a dynamic programming problem, such as Lion Swarm Optimization (LSO) [12], the greedy algorithm [13], model predictive control [14], and other methods, to learn the optimal strategy of agents. Traditional methods usually require complex mathematical calculations to solve multi-agent cooperative target search problems and are prone to easily falling into the local optimum. With the great success of deep learning in the field of artificial intelligence, more and more researchers use the method of deep reinforcement learning (DRL) to find the optimal search strategy. Deep reinforcement learning has been a hot topic in multi-agent target search in recent years [7; 15; 16; 17; 18]. DRL combines the perception ability of deep learning and the decision-making ability of reinforcement learning [19], and it provides a solution for the perception and decision-making problems of complex multi-agent systems. In 2017, [PERSON] et al.