An autonomous inter-task mapping learning method via artificial neural network for transfer learning

摘要

Transfer learning could speed up reinforcement learning in many applications. Toward the fully autonomous reinforcement learning transfer agent, the mapping between the source task and target task should be learned instead of human-designed. To this end, this paper proposes an autonomous intertask mapping learning method via artificial neural network, so as to reduce the human intervention in the transfer process. With this learned network, the reinforcement learning agent could transfer the learned knowledge from source task to target task for initialization, and set a good prior for the learning in the target task. The method is tested on the Keepaway Soccer Platform. The results indicate that the proposed method could provide a good jumpstart in the target task when weights are properly chosen for training the network.