weakness of the model. How would the values in table 1 change without this extra assumption? 3. I didn't find all parameter values. What are the model parameters for task 1? What lambda was chosen for the Boltzmann policy. But more importantly: How were the parameters chosen? Maximum likelihood estimates? 4. An answer to this point may be beyond the scope of this work, but it may be interesting to think about it. It is mentioned (lines 104-106) that "the examples [...] should maximally disambiguate the concept being taught from other possible concepts". How is disambiguation measured? How can disambiguation be maximized? Could there be an information theoretic approach to these questions? Something like: the teacher chooses samples that maximally reduce the entropy of the assumed posterior of the student. Does the proposed model do that? Minor points: â¢ line 88: The optimal policy is deterministic. Hence I'm a bit confused by "the stochastic optimal policy". Is above defined "the Boltzmann policy" meant? â¢ What is d and h in equation 2? â¢ line 108: "to calculate this ..." What is meant by "this"? â¢ Algorithm 1: Require should also include epsilon. Does line 1 initialize the set of policies to an empty set? Are the policies in line 4 added to this set? Does calculateActionValues return the Q* defined in line 75? What is M in line 6? How should p_min be chosen? Why is p_min needed anyway? â¢ Experiment 2: Is the reward 10 points (line 178) or 5 points (line 196)? â¢ Experiment 2: Is 0A the condition where all tiles are dangerous? Why are the likelihoods so much larger for 0A? Is it reasonable to average over likelihoods that differ by more than an order of magnitude (0A vs 2A-C)? â¢ Text and formulas should be carefully checked for typos (e.g. line 10 in Algorithm 1: delta > epsilon; line 217: 1^-6;)
3. I didn't find all parameter values. What are the model parameters for task 1? What lambda was chosen for the Boltzmann policy. But more importantly: How were the parameters chosen? Maximum likelihood estimates?