  Reinforcement learning algorithms, just like any other Machine learning
algorithm pose a serious threat from adversaries. The adversaries can
manipulate the learning algorithm resulting in non-optimal policies. In this
paper, we analyze the Multi-task Federated Reinforcement Learning algorithms,
where multiple collaborative agents in various environments are trying to
maximize the sum of discounted return, in the presence of adversarial agents.
We argue that the common attack methods are not guaranteed to carry out a
successful attack on Multi-task Federated Reinforcement Learning and propose an
adaptive attack method with better attack performance. Furthermore, we modify
the conventional federated reinforcement learning algorithm to address the
issue of adversaries that works equally well with and without the adversaries.
Experimentation on different small to mid-size reinforcement learning problems
show that the proposed attack method outperforms other general attack methods
and the proposed modification to federated reinforcement learning algorithm was
able to achieve near-optimal policies in the presence of adversarial agents.
