Abstract
The task allocation problem in a distributed environment is one of the most challenging problems in a multi-agent system. We propose a new task allocation process using deep reinforcement learning that allows cooperating agents to act automatically and learn how to communicate with other neighboring agents to allocate tasks and share resources. Through learning capabilities, agents will be able to reason conveniently, generate an appropriate policy and make a good decision. Our experiments show that it is possible to allocate tasks using deep Q-learning and more importantly show the performance of our distributed task allocation approach.