  The domain of safe multi-agent reinforcement learning (MARL), despite its
potential applications in areas ranging from drone delivery and vehicle
automation to the development of zero-energy communities, remains relatively
unexplored. The primary challenge involves training agents to learn optimal
policies that maximize rewards while adhering to stringent safety constraints,
all without the oversight of a central controller. These constraints are
critical in a wide array of applications. Moreover, ensuring the privacy of
sensitive information in decentralized settings introduces an additional layer
of complexity, necessitating innovative solutions that uphold privacy while
achieving the system's safety and efficiency goals. In this paper, we address
the problem of multi-agent policy optimization in a decentralized setting,
where agents communicate with their neighbors to maximize the sum of their
cumulative rewards while also satisfying each agent's safety constraints. We
consider both peak and average constraints. In this scenario, there is no
central controller coordinating the agents and both the rewards and constraints
are only known to each agent locally/privately. We formulate the problem as a
decentralized constrained multi-agent Markov Decision Problem and propose a
momentum-based decentralized policy gradient method, DePAint, to solve it. To
the best of our knowledge, this is the first privacy-preserving fully
decentralized multi-agent reinforcement learning algorithm that considers both
peak and average constraints. We then provide theoretical analysis and
empirical evaluation of our algorithm in a number of scenarios and compare its
performance to centralized algorithms that consider similar constraints.
