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Epsilon greedy is unaffected by scaling of rewards, it always selects a random action with a probability of epsilon. On the other hand, if we look at the formulation of UCB (Section 2.7 of Reinforcement Learning, Sutton and Barto): $$A_t \doteq \underset{a}{\operatorname{argmax}} [\mathcal{Q}_t(a) + c \sqrt{\frac{\ln t}{N_t(a)}}]$$ Where $Q_t(a)= \frac{R_1 +...


I think we give ourselves too much credit by already referring to our algorithms and machines as actually thinking and acting on motivations. In my opinion we still have a bit to go before we can actually refer to a human creation as thinking or being able to have motivations more then basic physical ones. By that I would say that a Machines' or AI ...


Motivating a robot to go right on one axis with one of its feet, is the simplest one I can think of for a legged robot.

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