Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning. (arXiv:2201.08520v1 [cs.LG])

Web: http://arxiv.org/abs/2201.08520

We present a two-step hybrid reinforcement learning (RL) policy that is
designed to generate interpretable and robust hierarchical policies on the RL
problem with graph-based input. Unlike prior deep reinforcement learning
policies parameterized by an end-to-end black-box graph neural network, our
approach disentangles the decision-making process into two steps. The first
step is a simplified classification problem that maps the graph input to an
action group where all actions share a similar semantic meaning. The second
step implements a sophisticated …

arxiv graph graph-based hybrid learning policy reinforcement learning