Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

Algorithm 1

Modified MADDPG for autonomous bus fleet control.

	for episode = 1 to M do
	for t = 1 to max-episode-length do
	for each agent i
	Execute action a =  and observe reward r and new state
	Store () in replay buffer
	for agent i = 1 to N do
	Sample a random mini batch of S samples () from
	Set
	Update critic by minimizing the loss
	Update actor using PPO stochastic PG
	end for
	Update target network parameters for each agent i
	end for
	end for