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Temporal-difference (TD) learning is an important field in reinforcementlearning. Sarsa and Q-Learning are among the most used TD algorithms. TheQ($\sigma$) algorithm (Sutton and Barto (2017)) unifies both. This paperextends the Q($\sigma$) algorithm to an online multi-step algorithm Q($\sigma,\lambda$) using eligibility traces and introduces Double Q($\sigma$) as theextension of Q($\sigma$) to double learning. Experiments suggest that the newQ($\sigma, \lambda$) algorithm can outperform the classical TD control methodsSarsa($\lambda$), Q($\lambda$) and Q($\sigma$).

Double Q($\sigma$) and Q($\sigma, \lambda$): Unifying Reinforcement Learning Control Algorithms