WalkTree( position p, history h ):
    # Handling non-player nodes
    if player( h ) == chance:
        sample action a according to \sigma_{chance}(h)
        return WalkTree( p, h + a )
    elif h == terminal:
        return utility
       
    # Computing the CFR strategy
    strategy s( h ) = regretMatching( h )
   
    # Handle the player which is optimized against
    if player( h ) == player_with_data:
        rho = data_precision( h )
        if U(0,1) < rho:
            s( h ) = data( h )
       
    # "Normal" ES CFR   
    if player( h ) == p:
        average_value = 0;
        for action a in possible_actions( h )
            values( a ) = WalkTree( p, h+a )
            average_value += s( h )( a ) * values( a )
        regret( h )( a ) += values( a ) - average_value
       
    elif player( h ) !=p:
        for action a in possible_actions( h ):
            average_strategy( h )( a ) += s( h )( a )
           
        sample action a according to s( h )
        return WalkTree( p, h+a )