  Ensuring safety in dynamic multi-agent systems is challenging due to limited
information about the other agents. Control Barrier Functions (CBFs) are
showing promise for safety assurance but current methods make strong
assumptions about other agents and often rely on manual tuning to balance
safety, feasibility, and performance. In this work, we delve into the problem
of adaptive safe learning for multi-agent systems with CBF. We show how
emergent behavior can be profoundly influenced by the CBF configuration,
highlighting the necessity for a responsive and dynamic approach to CBF design.
We present ASRL, a novel adaptive safe RL framework, to fully automate the
optimization of policy and CBF coefficients, to enhance safety and long-term
performance through reinforcement learning. By directly interacting with the
other agents, ASRL learns to cope with diverse agent behaviours and maintains
the cost violations below a desired limit. We evaluate ASRL in a multi-robot
system and a competitive multi-agent racing scenario, against learning-based
and control-theoretic approaches. We empirically demonstrate the efficacy and
flexibility of ASRL, and assess generalization and scalability to
out-of-distribution scenarios. Code and supplementary material are public
online.
