Model Selection, Reinforcement Learning, Deep Learning.
probability ε and the best action is chosen with probability (1-ε) Upper Confidence Bound Policy: In Upper bound Policy, action is selected according to formula: Nₜ = argminₐ [Qₜ (a) + c sqrt(ln t / Nₜ(a))] where ln t denotes the natural logarithm of t, Nₜ(a) denotes the number of times that action