A reinforcement learning algorithm for a class of dynamical environments using neural networks

In many conventional approaches, when the environment is dynamically varied for agents, the models of agents are retrained in order to adapt to the current environment. However, when the same environments reappear in the future, it is not efficient to discard or modify the current model. To learn efficiently in this situation, we present new agent architecture. In this paper, we added extra models to the RAN-LTM agent model so that it can work well under a class of dynamic environments. In order to adapt rapidly to dynamic environments, it might be natural to consider that agents possess capability to store only essential knowledge, capability to retrieve proper knowledge, capability to detect environmental changes accurately.