  Recent advances in learning techniques have garnered attention for their
applicability to a diverse range of real-world sequential decision-making
problems. Yet, many practical applications have critical constraints for
operation in real environments. Most learning solutions often neglect the risk
of failing to meet these constraints, hindering their implementation in
real-world contexts. In this paper, we propose a risk-aware decision-making
framework for contextual bandit problems, accommodating constraints and
continuous action spaces. Our approach employs an actor multi-critic
architecture, with each critic characterizing the distribution of performance
and constraint metrics. Our framework is designed to cater to various risk
levels, effectively balancing constraint satisfaction against performance. To
demonstrate the effectiveness of our approach, we first compare it against
state-of-the-art baseline methods in a synthetic environment, highlighting the
impact of intrinsic environmental noise across different risk configurations.
Finally, we evaluate our framework in a real-world use case involving a 5G
mobile network where only our approach consistently satisfies the system
constraint (a signal processing reliability target) with a small performance
toll (8.5% increase in power consumption).
