On The Transferability of Deep-Q Networks

Transfer Learning (TL) is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks, ranging from long training times to the needs of large datasets. While exploiting TL is a well established and successful training practice in Supervised Learning (SL), its applicability in Deep Reinforcement Learning (DRL) is rarer. In this paper, we study the level of transferability of three different variants of Deep-Q Networks on popular DRL benchmarks as well as on a set of novel, carefully designed control tasks. Our results show that transferring neural networks in a DRL context can be particularly challenging and is a process which in most cases results in negative transfer. In the attempt of understanding why Deep-Q Networks transfer so poorly, we gain novel insights into the training dynamics that characterizes this family of algorithms. Over the last years, the marriage between Reinforcement Learning (RL) algorithms and deep neural networks, commonly denoted as Deep Reinforcement Learning (DRL) [François-Lavet et al., 2018] has gained tremendous attention [Henderson et al., 2018]. Neural networks have, in fact, proven to be extremely successful both in a model-free RL setting as in a model-based one.