Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning

Transferring learned knowledge from one environment to another is an important step towards practical reinforcement learning (RL). In this paper, we investigate the problem of transfer learning across environments with different dynamics while accomplishing the same task in the continuous control domain. We start by illustrating the limitations of policy-centric methods (policy gradient, actor- critic, etc.) when transferring knowledge across environments. We then propose a general model-based value-centric (MVC) framework for continuous RL. MVC learns a dynamics approximator and a value approximator simultaneously in the source domain, and makes decision based on both of them. We evaluate MVC against popular baselines on 5 benchmark control tasks in a training from scratch setting and a transfer learning setting. Our experiments demonstrate MVC achieves comparable performance with the baselines when it is trained from scratch, while it significantly surpasses them when it is used in the transfer setting.