  Optimization of first-passage times is required in applications ranging from
nanobots navigation to market trading. In such settings, one often encounters
unevenly distributed noise levels across the environment. We extensively study
how a learning agent fares in 1- and 2- dimensional heated gridworlds with an
uneven temperature distribution. The results show certain bias effects in
agents trained via simple tabular Q-learning, SARSA, Expected SARSA and Double
Q-learning. While high learning rate prevents exploration of regions with
higher temperature, low enough rate increases the presence of agents in such
regions. The discovered peculiarities and biases of temporal-difference-based
reinforcement learning methods should be taken into account in real-world
physical applications and agent design.
