engleski

Q-learning by the nth step state and multi-agent negotiation in unknown environment

This work will show a new procedure of Q-learning in which the agent’s decision, regarding the next step, is not based on the optimal action at that moment but on the usefulness of a future state. A near agent communication has been implemented so that the agents signal each other their future actions which contribute to a better choice of actions for each of the agents. The new method is named Q-learning by the nth step and multi-agent negotiation. The results of the testing of this algorithm are compared with the basic QL algorithm which is also graphically demonstrated and the advantages of the new algorithm are listed too. An average of 40 % collision decrease is obtained during learning procedure.

agent; learning from reward and punishment; q-learning; reinforcement learning

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