MoVEMo - A structured approach for engineering reward functions
Paper i proceeding, 2018

Reinforcement learning (RL) is a machine learning technique that has been increasingly used in robotic systems. In reinforcement learning, instead of manually pre-program what action to take at each step, we convey the goal of a software agent in terms of reward functions. The agent tries different actions in order to maximize a numerical value, i.e. the reward. A misspecified reward function can cause problems such as reward hacking, where the agent finds out ways that maximize the reward without achieving the intended goal.

As RL agents become more general and autonomous, the design of reward functions that elicit the desired behaviour in the agent becomes more important and cumbersome. In this paper, we present a technique to formally express reward functions in a structured way; this stimulates a proper reward function design and as well enables the formal verification of it. We start by defining the reward function using state machines. In this way, we can statically check that the reward function satisfies certain properties, e.g., high-level requirements of the function to learn. Later we automatically generate a runtime monitor which runs in parallel with the learning agent-that provides the rewards according to the definition of the state machine and based on the behaviour of the agent.

We use the Uppaal model checker to design the reward model and verify the TCTL properties that model high-level requirements of the reward function and Larva to monitor and enforce the reward model to the RL agent at runtime.

reward function


runtime monitoring

reinforcement learning,


Piergiuseppe Mallozzi

Chalmers, Data- och informationsteknik, Software Engineering

Raul Pardo Jimenez

Chalmers, Data- och informationsteknik, Formella metoder

Vincent Duplessis

ENSICAEN Ecole Nationale Superieure d'Ingenieurs de Caen

Patrizio Pelliccione

Chalmers, Data- och informationsteknik, Software Engineering

Gerardo Schneider

Chalmers, Data- och informationsteknik, Formella metoder

Proceedings - 2nd IEEE International Conference on Robotic Computing, IRC 2018

Vol. 2018 250-257
978-1-5386-4651-9 (ISBN)

2018 Second IEEE International Conference on Robotic Computing (IRC)
Laguna Hills, CA, USA,


Informations- och kommunikationsteknik


Robotteknik och automation

Datavetenskap (datalogi)




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