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Dingyuanhao Sun

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Off-Policy Temporal Difference Learning with Bellman Residuals

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By Shangdong Yang, Xingguo Chen, Dingyuanhao Sun
| MDPI
1. Introduction Reinforcement Learning (RL) refers to the process where an agent learns how to behave optimally by observing the effects of its actions in an environment and adjusting its behavior accordingly [1]. However, how can one learn from experiences generated by different behavior strategies while ensuring continuous improvement? This issue is known as the off-policy learning problem [2,3].
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