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In this article, we address a form of active perception characterized by curiosity-driven, open-ended exploration with intrinsic motivation, carried out by a group of agents. The multiple agents and a large number of possible actions are the main motivation for incorporating Multi-Agent Reinforcement Learning used to train a neural network in order to derive agent&#x2019;s policy. Partially Observable Markov Decision Process framework is used to accommodate the inaccuracy of sensors and probabilistic nature of agent&#x2019;s actions. The proposed method incorporates a consensus that derives the common belief vector, thus allowing each agent to make its decisions based on information acquired by all agents involved in the process of active perception. A well-known benchmark problem with a decentralized tiger scenario was used to demonstrate the possibility of the method to generate agents with different perceptual characteristics by simply changing the agents&#x2019; reward function related to their intrinsic motivation. The main validation of the proposed approach was performed by using an example of multi-agent search mission. Final results are presented and discussed, and possible avenues for progress on open problems are identified.

Multi-Agent Active Perception Based on Reinforcement Learning and POMDP