Author ORCID Identifier
Thesis - Open Access
Bachelor of Arts
Reinforcement learning, Multiagnet systems, Artificial intelligence, Open environment, Deep reinforcement learning, Neural networksm, Markov decision process
In open multiagent systems, multiple agents work together or compete to reach the goal while members of the group change over time. For example, intelligent robots that are collaborating to put out wildfires may run out of suppressants and have to leave the place to recharge; the rest of the robots may need to change their behaviors accordingly to better control the fires. Thus, openness requires agents not only to predict the behaviors of others, but also the presence of other agents. We present a deep reinforcement learning method that adapts the proximal policy optimization algorithm to learn the optimal actions of an agent in open multiagent environments. We demonstrate how openness can be incorporated into state-of-the-art reinforcement learning algorithms. Simulations of wildfire suppression problems show that our approach enables the agents to learn the legal actions.
Zhu, Tianxing, "Deep Reinforcement Learning for Open Multiagent System" (2022). Honors Papers. 845.