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In this work a novel approach to Transfer Learning for the use in Deep Reinforcement Learning is introduced. The agent is realized as an actor-critic framework, namely the Deep Deterministic Policy Gradient algorithm. The Q-function and the policy are represented as deep feed-forward networks, that are trained by minimizing the mean squared Bellman error and by maximizing the expected reward, respectively...
During decision making and acting in the environment humans appraise decisions and observations with feelings and emotions. In this paper we propose a framework to incorporate an emotional model into the decision making process of a machine learning agent. We use a hierarchical structure to combine reinforcement learning with a dimensional emotional model. The dimensional model calculates two dimensions...
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