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Gradient-following algorithms are deployed for efficient adaptation of exploration parameters in temporal-difference learning with discrete action spaces. Global and local variants are evaluated in discrete and continuous state spaces. The global variant is memory efficient in terms of requiring exploratory data only for starting states. In contrast, the local variant requires exploratory data for each state of the state space, but produces exploratory behavior only in states with improvement potential. Our results suggest that gradient-based exploration can be efficiently used in combination with off- and on-policy algorithms such as Q-learning and Sarsa.

Gradient Algorithms for Exploration/Exploitation Trade-Offs: Global and Local Variants