This problem presents a brief glimpse of the problems that can arise in off$-$policy learning with function approximation, through the concepts that have been introduced so far. If you would like a more detailed discussion on these issues, you may refer to Chapter $11.$ Let us now apply semi$-$gradient TD learning from Chapter $9$ with batch updates $($Section $6\mathrm{.}3)$ to the following value$-$function approximation problem, based on a problem known as Baird's Counterexample:This problem presents a brief glimpse of the problems that can arise in off$-$policy learning with function approximation, through the concepts that have been introduced so far. If you would like a more detailed discussion on these issues, you may refer to Chapter $11.$ Let us now apply semi$-$gradient TD learning from Chapter $9$ with batch updates $($Section $6\mathrm{.}3)$ to the following value$-$function approximation problem, based on a problem known as Baird's Counterexample: