Evaluating the performance of a reinforcement learning (RL) agent typically involves measuring its ability to achieve a desired goal over time. One common approach is to utilize cumulative reward, which is the total sum of rewards collected by the agent during its interactions with the environment. This measure provides a straightforward quantitative assessment: a higher cumulative reward indicates better performance. Developers can also assess the average reward per episode, which helps in understanding how the agent is improving over successive trials. For example, if an agent consistently achieves higher rewards in later episodes of training, this indicates successful learning.
Another important aspect of performance evaluation is stability and convergence. Developers should look at the variance in the agent's rewards over time, as significant fluctuations can signify that the agent is not adequately learning or generalizing from its experiences. A well-performing agent should show a trend of increasing reward stability as training progresses. Visualizing the training process with plots of cumulative reward or average reward per episode can aid in diagnosing issues. If the agent’s performance plateaus or declines, it may indicate that the learning rate is too high or that the exploration strategy needs adjustment.
Lastly, conducting performance evaluations in diverse scenarios or environments is vital. This ensures that the agent is not only performing well in specific training conditions but also generalizing its learning. Developers can use metrics like the agent’s performance in novel states or unseen environments to assess robustness. For example, if an agent trained in a video game can successfully navigate a previously unknown level, it indicates a strong understanding of the game's mechanics. Ultimately, combining these metrics provides a comprehensive view of the agent's capabilities, helping developers refine their algorithms effectively.