  The aim of multi-agent reinforcement learning systems is to provide
interacting agents with the ability to collaboratively learn and adapt to the
behavior of other agents. In many real-world applications, the agents can only
acquire a partial view of the world. However, in realistic settings, one or
more agents that show arbitrarily faulty or malicious behavior may suffice to
let the current coordination mechanisms fail. In this paper, we study a
practical scenario considering the security issues in the presence of agents
with arbitrarily faulty or malicious behavior. Under these circumstances,
learning an optimal policy becomes particularly challenging, even in the
unrealistic case that an agent's policy can be made conditional upon all other
agents' observations. To overcome these difficulties, we present an
Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and
relevant information for each agent at every time-step. The multi-head
attention mechanism enables the agents to learn effective communication
policies through experience concurrently to the action policies. Empirical
results have shown that FT-Attn beats previous state-of-the-art methods in some
complex environments and can adapt to various kinds of noisy environments
without tuning the complexity of the algorithm. Furthermore, FT-Attn can
effectively deal with the complex situation where an agent needs to reach
multiple agents' correct observation at the same time.
