Learning to Plan with Logical Automata

Abstract

This paper introduces the Logic-based Value Iteration Network (LVIN) framework, which combines imitation learning and logical automata to enable agents to learn complex behaviors from demonstrations. We address two problems with learning from expert knowledge: (1) how to generalize learned policies for a task to larger classes of tasks, and (2) how to account for erroneous demonstrations. Our LVIN model solves finite gridworld environments by instantiating a recurrent, convolutional neural network as a value iteration procedure over a learned Markov Decision Process (MDP) that factors into two MDPs: a small finite state automaton (FSA) corresponding to logical rules, and a larger MDP corresponding to motions in the environment. The parameters of LVIN (value function, reward map, FSA transitions, large MDP transitions) are approximately learned from expert trajectories. Since the model represents the learned rules as an FSA, the model is interpretable; since the FSA is integrated into planning, the behavior of the agent can be manipulated by modifying the FSA transitions. We demonstrate these abilities in several domains of interest, including a lunchboxpacking manipulation task and a driving domain.