inline">\(10^4\)</span> walks for the benchmark policies. Thus, we save the agent’s policy at different stages of the training and then, in a postlearning analysis, we run <span class="math inline">\(10^4\)</span> walks with that frozen policy to get a more accurate evaluation of its performance.</p>
<p>This performance analysis is done with the method <code>[`agent_efficiency`](https://gorkamunoz.github.io/rl_opts/lib_nbs/learning_and_benchmark.html#agent_efficiency)</code>, which is imported by running: