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arXiv · 2201.00286

Reinforcement Learning for Task Specifications with Action-Constraints

Arun Raman·Keerthan Shagrithaya·Shalabh Bhatnagar
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Abstract

In this paper, we use concepts from supervisory control theory of discrete event systems to propose a method to learn optimal control policies for a finite-state Markov Decision Process (MDP) in which (only) certain sequences of actions are deemed unsafe (respectively safe). We assume that the set of action sequences that are deemed unsafe and/or safe are given in terms of a finite-state automaton; and propose a supervisor that disables a subset of actions at every state of the MDP so that the constraints on action sequence are satisfied. Then we present a version of the Q-learning algorithm for learning optimal policies in the presence of non-Markovian action-sequence and state constraints, where we use the development of reward machines to handle the state constraints. We illustrate the method using an example that captures the utility of automata-based methods for non-Markovian state and action specifications for reinforcement learning and show the results of simulations in this setting.

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BibTeXRIS

Arun Raman, Keerthan Shagrithaya, Shalabh Bhatnagar. 2022-01-02. Reinforcement Learning for Task Specifications with Action-Constraints. https://arxiv.org/abs/2201.00286

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