  This paper considers the policy evaluation problem in a multi-agent
reinforcement learning (MARL) environment over decentralized and directed
networks. The focus is on decentralized temporal difference (TD) learning with
linear function approximation in the presence of unreliable or even malicious
agents, termed as Byzantine agents. In order to evaluate the quality of a fixed
policy in a common environment, agents usually run decentralized TD($\lambda$)
collaboratively. However, when some Byzantine agents behave adversarially,
decentralized TD($\lambda$) is unable to learn an accurate linear approximation
for the true value function. We propose a trimmed-mean based
Byzantine-resilient decentralized TD($\lambda$) algorithm to perform policy
evaluation in this setting. We establish the finite-time convergence rate, as
well as the asymptotic learning error in the presence of Byzantine agents.
Numerical experiments corroborate the robustness of the proposed algorithm.
