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…
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Access:
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Date:
May 2019
Creator:
Howe, Dustin