Convergence of Optimistic and Incremental Q-Learning
We show the convergence of two deterministic variants of Q-learning. The first is the widely used optimistic Q-learning, which initializes the Q-values to large initial values and then follows a greedy policy with respect to the Q-values. We show that setting the initial value sufficiently large guarantees the converges to an e-optimal policy. The second is a new and novel algorithm incremental Q-learning, which gradually promotes the values of actions that are not taken. We show that incremental Q-learning converges, in the limit, to the optimal policy Our incremental Q-learning algorithm can be viewed as derandomization of the e-greedy Q-learning.