Proof-of-Stake (PoS) is rapidly getting popular in blockchain networks as they require far less energy than traditional Proof-of-Work systems. However, PoS still faces critical challenges. Security risks such as long-range attacks, Sybil threats, and the nothing-at-stake dilemma continue to challenge these networks. This paper introduces a novel approach to validator management in PoS systems by applying machine learning—specifically multi-agent reinforcement learning—to help improve how validators are chosen and monitored over time.
Building on this approach, we apply machine learning—specifically multi-agent reinforcement learning (MARL)—to enhance how validators are dynamically selected and monitored over time. Unlike prior works, our approach introduces a trust scoring mechanism that goes beyond basic stake or uptime metrics by learning from validator behavior patterns and adapting to evolving threats.
A key novelty of this work lies in the integration of Explainable AI (XAI) techniques. Alongside the MARL framework, the system provides human-understandable justifications for validator selection and penalties, addressing the current gap in explainability and auditability within existing AI-driven blockchain solutions.The core idea is to build a system that learns from validator activity and uses that knowledge to make better decisions about which nodes should participate in block validation. What sets this paper apart is the focus on explainability. Alongside the AI models, the system will include methods for making those decisions understandable to users and developers. By integrating explainable AI (XAI), the goal is not just smarter validator selection but also one that is transparent and easier to audit.
This research aims to lay the groundwork for a more secure, adaptive, and trustworthy PoS framework—one that not only mitigates known threats but also evolves with the network, all while upholding the principles of decentralization, transparency, and user trust that define blockchain technology.
from codes.environment.pos_env import PoSEnvironment
from codes.agent.marl_agent import MARLAgent
# Retrieve simulation configuration
import codes.config as config
# Step 1: Initialize the Environment
env = PoSEnvironment()
# Step 2: Define agent configuration
agent_config = {
"learning_rate": config.LEARNING_RATE,
"gamma": config.DISCOUNT_FACTOR,
"epsilon": config.EPSILON_START,
"action_space": 3 # For example: [Propose, Abstain, Attest]
}
# Step 3: Create MARL Agents
agents = [MARLAgent(i, agent_config) for i in range(config.NUM_VALIDATORS)]
# Step 4: Run a basic simulation loop
NUM_EPISODES = 10
for episode in range(NUM_EPISODES):
state = env.get_state()
for agent in agents:
agent.observe_state(state[agent.agent_id])
action = agent.select_action()
agent.set_last_action(action)
rewards, next_state, done = env.step(actions)
for agent in agents:
agent.update(next_state[agent.agent_id], reward, done)
agent.update_trust_score(uptime, missed_blocks, slashed)
# Step 5: Access trained agent policies and trust scores
for agent in agents[:5]: # Show first 5 agents
print(f"Agent {agent.agent_id} - Trust Score: {agent.trust}")