  This paper establishes directionality reinforcement learning (DRL) technique
to propose the complete decentralized multi-agent reinforcement learning method
which can achieve cooperation based on each agent's learning: no communication
and no observation. Concretely, DRL adds the direction "agents have to learn to
reach the farthest goal among reachable ones" to learning agents to operate the
agents cooperatively. Furthermore, to investigate the effectiveness of the DRL,
this paper compare Q-learning agent with DRL with previous learning agent in
maze problems. Experimental results derive that (1) DRL performs better than
the previous method in terms of the spending time, (2) the direction makes
agents learn yielding action for others, and (3) DRL suggests achieving
multiagent learning with few costs for any number of agents.
