@InProceedings{chen_144,
    title = {Modified Retrace for Off-Policy Temporal Difference Learning},
    author = {Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, and Yang Gao},
    abstract = {Off-policy learning is a key to extend reinforcement learning as it allows to learn  a target policy from a different behavior policy that generates the data. However, it is well known as ``the deadly triad'' when combined with bootstrapping and function approximation. Retrace is an efficient and  convergent off-policy algorithm with tabular value functions which employs  truncated importance sampling ratios. Unfortunately, Retrace is known to be unstable with linear function approximation. In this paper, we propose modified Retrace  to correct the  off-policy return, derive a new off-policy temporal difference learning algorithm (TD-MRetrace) with linear function approximation, and obtain a convergence guarantee under standard assumptions. Experimental results on counterexamples and control tasks validate the effectiveness of the proposed algorithm compared with traditional algorithms.}
}

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