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arXiv · 2111.11768

Schedule Based Temporal Difference Algorithms

Rohan Deb·Meet Gandhi·Shalabh Bhatnagar
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Abstract

Learning the value function of a given policy from data samples is an important problem in Reinforcement Learning. TD($λ$) is a popular class of algorithms to solve this problem. However, the weights assigned to different $n$-step returns in TD($λ$), controlled by the parameter $λ$, decrease exponentially with increasing $n$. In this paper, we present a $λ$-schedule procedure that generalizes the TD($λ$) algorithm to the case when the parameter $λ$ could vary with time-step. This allows flexibility in weight assignment, i.e., the user can specify the weights assigned to different $n$-step returns by choosing a sequence $\{λ_t\}_{t \geq 1}$. Based on this procedure, we propose an on-policy algorithm - TD($λ$)-schedule, and two off-policy algorithms - GTD($λ$)-schedule and TDC($λ$)-schedule, respectively. We provide proofs of almost sure convergence for all three algorithms under a general Markov noise framework.

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Rohan Deb, Meet Gandhi, Shalabh Bhatnagar. 2021-11-23. Schedule Based Temporal Difference Algorithms. https://arxiv.org/abs/2111.11768

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