Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Off-policy evaluation is the problem of evaluating a decision-making policy using data collected under a different behaviour policy. While several methods are available for addressing off-policy evaluation, little work has been done on identifying the best methods. ,  conduct an in-depth comparative study of several off-policy evaluation methods in non-bandit, finite-horizon MDPs, using randomly generated MDPs, as ll as a Mallard population dynamics model [Anderson, 1975] .  find that un-normalized importance sampling can exhibit prohibitively large variance in problems involving look-ahead longer than a few time steps, and that dynamic programming methods perform better than Monte-Carlo style methods.
['Doina Precup', 'Joelle Pineau']
I am writing a research paper on a idea An Empirical Analysis of Off-policy Learning in Discrete MDPs
[{"from": "human", "value": "I am writing a research paper on a idea An Empirical Analysis of Off-policy Learning in Discrete MDPs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Off-policy evaluation is the problem of evaluating a decision-making policy using data collected under a different behaviour policy. While several methods are available for addressing off-policy evaluation, little work has been done on identifying the best methods. ,  conduct an in-depth comparative study of several off-policy evaluation methods in non-bandit, finite-horizon MDPs, using randomly generated MDPs, as ll as a Mallard population dynamics model [Anderson, 1975] .  find that un-normalized importance sampling can exhibit prohibitively large variance in problems involving look-ahead longer than a few time steps, and that dynamic programming methods perform better than Monte-Carlo style methods."}]