(self, agent_seat_id, n_iterations, mode, stack_size)
returns an estimate of a lower bound of the exploitablity of the agent
returns an estimate of a lower bound of the exploitablity of the agent
returns an estimate of a lower bound of the exploitablity of the agent
def run(self, agent_seat_id, n_iterations, mode, stack_size):
        """ returns an estimate of a lower bound of the exploitablity of the agent """

        self.agent.set_mode(mode=mode)
        self.agent.to_stack_size(stack_size)
        self.agent_range.reset()

        self._env = self._eval_env_bldr.get_new_env(is_evaluating=True, stack_size=stack_size)

        if not self.agent.can_compute_mode():
            return None

        if self._eval_env_bldr.env_cls.IS_FIXED_LIMIT_GAME:
            return self._run_limit(agent_seat_id=agent_seat_id, n_iterations=n_iterations)
        else:
            return self._run_no_limit(agent_seat_id=agent_seat_id, n_iterations=n_iterations)
def run ( self , agent_seat_id , n_iterations , mode , stack_size ) : self . agent . set_mode ( mode = mode ) self . agent . to_stack_size ( stack_size ) self . agent_range . reset ( ) self . _env = self . _eval_env_bldr . get_new_env ( is_evaluating = True , stack_size = stack_size ) if not self . agent . can_compute_mode ( ) : return None if self . _eval_env_bldr . env_cls . IS_FIXED_LIMIT_GAME : return self . _run_limit ( agent_seat_id = agent_seat_id , n_iterations = n_iterations ) else : return self . _run_no_limit ( agent_seat_id = agent_seat_id , n_iterations = n_iterations )