  The core of a blockchain network is its consensus algorithm. Starting with
the Proof-of-Work, there have been various versions of consensus algorithms,
such as Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Practical Byzantine
Fault Tolerance (PBFT). Each of these algorithms focuses on different aspects
to ensure efficient and reliable processing of transactions. Blockchain
operates in a decentralized manner where there is no central authority and the
network is composed of diverse users. This openness creates the potential for
malicious nodes to disrupt the network in various ways. Therefore, it is
crucial to embed a mechanism within the blockchain network to constantly
monitor, identify, and eliminate these malicious nodes. However, there is no
one-size-fits-all mechanism to identify all malicious nodes. Hence, the dynamic
adaptability of the blockchain network is important to maintain security and
reliability at all times. This paper introduces MRL-PoS, a Proof-of-Stake
consensus algorithm based on multi-agent reinforcement learning. MRL-PoS
employs reinforcement learning for dynamically adjusting to the behavior of all
users. It incorporates a system of rewards and penalties to eliminate malicious
nodes and incentivize honest ones. Additionally, MRL-PoS has the capability to
learn and respond to new malicious tactics by continually training its agents.
