iences.
2. Sample **N** experiences from \\(\mathcal{D}\\) at random and update the Q function.

**Clamping TD Error**. Another interesting feature is to clamp the TD Error gradient at some fixed maximum value. That is, if the TD Error \\(r\_t + \gamma \max\_a Q(s\_{t+1}, a) - Q(s\_t, a\_t)\\) is greater in magnitude then some threshold `spec.tderror_clamp`, then we cap it at that value. This makes the learning more robust to outliers and has the interpretation of using Huber loss, which is an L2 penalty in