Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2023

Abstract

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challenges, first, we associate safety signals with state-action trajectories (rather than just immediate state-action). This makes our safety model highly general. We also assume categorical safety labels are given for different trajectories, rather than a numerical cost function, which is harder to specify by the problem designer. We then employ a supervised learning model to learn such non-Markovian safety patterns. Second, we develop a Lagrange multiplier method, which incorporates the safety model and the underlying MDP model in a single computation graph to facilitate agent learning of safe behaviors. Finally, our empirical results on a variety of discrete and continuous domains show that this approach can satisfy complex non-Markovian safety constraints while optimizing agent's total returns, is highly scalable, and is also better than previous best approach for Markovian NSEs.

Keywords

Artificial intelligence, Cost functions, Lagrange multipliers, Learning systems

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings International Conference on Automated Planning and Scheduling, ICAPS

ISBN

9781577358817

Identifier

10.1609/icaps.v33i1.27241

City or Country

Prague, Czech Republic

Download

Share

COinS