Accelerated Target Updates for Q-learning
This work addresses convergence speed issues in reinforcement learning for practitioners, but it is incremental as it builds on momentum-based optimization methods.
The paper tackles slow convergence in Q-learning by proposing an accelerated target update scheme using historical Q-function iterates, showing improved convergence performance in tests like FrozenLake, LQR problems, and Atari games.
This paper studies accelerations in Q-learning algorithms. We propose an accelerated target update scheme by incorporating the historical iterates of Q functions. The idea is conceptually inspired by the momentum-based accelerated methods in the optimization theory. Conditions under which the proposed accelerated algorithms converge are established. The algorithms are validated using commonly adopted testing problems in reinforcement learning, including the FrozenLake grid world game, two discrete-time LQR problems from the Deepmind Control Suite, and the Atari 2600 games. Simulation results show that the proposed accelerated algorithms can improve the convergence performance compared with the vanilla Q-learning algorithm.