Re:AlphaGo Beats Lee Sedol in Final Game
by skj on Tuesday March 15, 2016 @08:29AM
(#11289091)
"and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function."
To be clear, the above refers to specific concepts in Reinforcement Learning.
A policy is a function from state (in Go, where all the stones are) to action (where to place the next stone). I agree that it is unlikely to have an effective policy function. At least one that is calculated efficiently (no tree search)... otherwise its not what a Reinforcement Learning researcher typically calls a policy function.
A value function is is a function from state to numerical "goodness", and is more or less one step removed from a policy function: you can choose the action that takes you to the state with the highest value. It has the same representational problems found there.
To be clear, the above refers to specific concepts in Reinforcement Learning.
A policy is a function from state (in Go, where all the stones are) to action (where to place the next stone). I agree that it is unlikely to have an effective policy function. At least one that is calculated efficiently (no tree search)... otherwise its not what a Reinforcement Learning researcher typically calls a policy function.
A value function is is a function from state to numerical "goodness", and is more or less one step removed from a policy function: you can choose the action that takes you to the state with the highest value. It has the same representational problems found there.