Abstract
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common solution is to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states. However, designing reward or cost functions to incentivise safe behaviours with maximal probability is non-trivial and can increase with the complexity of the problem. To address this, we propose a simple algorithm for agents to automatically learn the penalty for unsafe transitions while learning to solve the given task. Precisely, the agent keeps track of its lowest and highest estimated state-values, then simply uses their difference as the penalty for unsafe transitions. Importantly, our approach can be used with any base RL algorithm since it only modifies the rewards perceived by an agent, and introduces no new hyperparameters that would require tuning. Our experiments demonstrate the effectiveness of this approach in enabling agents to learn safe policies in high-dimensional continuous control environments.
Geraud Nangue Tasse
Lecturer
I am interested in reinforcement learning (RL) since it is the subfield of machine learning with the most potential for achieving AGI.
Tamlin Love
PhD Student
I am a PhD student at the Institut de Robotica i Informàtica Industrial (IRI) (under CSIC and UPC) in Barcelona, working on the TRAIL Marie Skłodowska-Curie Doctoral Network under the supervision of Guillem Alenyà. I was previously an MSc student and lecturer at the University of the Witwatersrand, under the supervision of Benjamin Rosman and Ritesh Ajoodha, as well as a member of the RAIL Lab.
Benjamin Rosman
Lab Director
I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.