Search Results

Note: All results matching your query require you to be a member of the UNT Community (you must be on campus or login with university credentials for access).

Proximal Policy Optimization in StarCraft

Description: Deep reinforcement learning is an area of research that has blossomed tremendously in recent years and has shown remarkable potential in computer games. Real-time strategy game has become an important field of artificial intelligence in game for several years. This paper is about to introduce a kind of algorithm that used to train agents to fight against computer bots. Not only because games are excellent tools to test deep reinforcement learning algorithms for their valuable insight into how w… more
Date: May 2019
Creator: Liu, Yuefan

Quantile Regression Deep Q-Networks for Multi-Agent System Control

Description: Training autonomous agents that are capable of performing their assigned job without fail is the ultimate goal of deep reinforcement learning. This thesis introduces a dueling Quantile Regression Deep Q-network, where the network learns the state value quantile function and advantage quantile function separately. With this network architecture the agent is able to learn to control simulated robots in the Gazebo simulator. Carefully crafted reward functions and state spaces must be designed for… more
Date: May 2019
Creator: Howe, Dustin
Back to Top of Screen