Reinforcement Learning Your Way: Agent Characterization through Policy Regularization. (arXiv:2201.10003v1 [cs.LG])

Web: http://arxiv.org/abs/2201.10003

The increased complexity of state-of-the-art reinforcement learning (RL)
algorithms have resulted in an opacity that inhibits explainability and
understanding. This has led to the development of several post-hoc
explainability methods that aim to extract information from learned policies
thus aiding explainability. These methods rely on empirical observations of the
policy and thus aim to generalize a characterization of agents' behaviour. In
this study, we have instead developed a method to imbue a characteristic
behaviour into agents' policies through regularization of …

arxiv learning policy reinforcement learning