Upper-Bound Clipping Function for Policy Ratios

A policy optimization algorithm uses a bounding function, bound(value, lower_bound, upper_bound), to constrain a ratio of action probabilities. This function clips the value to ensure it stays within the interval [lower_bound, upper_bound]. If the ratio value is 1.5, and the interval is defined by a parameter ε = 0.2 (i.e., the interval is [1 - 0.2, 1 + 0.2]), what is the resulting value after the bounding operation is applied?

In a policy optimization algorithm, a ratio comparing the likelihood of an action under a new policy versus an old policy is constrained to stay within the interval [1-ε, 1+ε]. What is the most likely consequence of setting the parameter ε to a very small value (e.g., 0.01)?

Applying a Bounding Constraint on Probability Ratios