Behavioral Decision-Making of Mobile Robot in Unknown Environment with the Cognitive Transfer

How to improve the behavioral decision-making ability and adaptability to unknown environments is of great importance for an agent. The traditional decision-making methods usually suffer from long training time, due to the large amount of training samples, or low adaptability to the unknown environments, or lack of the continuous learning capacities, etc. In response to these problems, this work proposes a novel motivated developmental network (MDN) to improve the decision-making ability of the agent. During the environment exploration, if the agent encounters an unknown environment, new layers and neurons are dynamically inserted to the MDN, according to the task requirements. Through the interaction between internal neurons and the inserted new neurons, the agent can autonomously develop and learn in the unknown environments without training data, but the behavioral decision-making at this stage is random. To further improve the agent’s decision-making ability, in the off-task process, through the gated self-organization mechanism, the agent can selectively recall the specific knowledge in its “brain”, and the MDN will transfer and generate a large amount of new data, according to the recalled knowledge, then the new layers and neurons will be inserted to memorize the new knowledge. Hence the knowledge base of the MDN becomes more and more complete, thereby improving its decision-making ability and adaptability to the new environment. To demonstrate the performance of the MDN model, a mobile robot navigation in different environments are executed. The experimental results illustrate that the agent can not only autonomously learn in static environments, but also has better decision-making ability in unknown dynamic environment, i.e., better adaptability to the new environment. Comparison with other algorithms further demonstrate the potential of the proposed MDN model.