Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poorly understood in practice. We discuss different analysis techniques and assess their effectiveness for investigating the impact of action representations in RL. Our experiments demonstrate that the action representation can significantly influence the learning performance on popular RL benchmark tasks. The analysis results indicate that some of the performance differences can be attributed to changes in the complexity of the optimization landscape. Finally, we discuss open challenges of analysis techniques for RL algorithms.

翻译：强化学习（RL）是学习解决复杂现实任务的多功能框架。然而，实践中RL算法学习性能的影响因素往往难以充分理解。我们讨论了不同的分析技术，并评估了它们在研究RL中动作表示影响的有效性。实验表明，动作表示会显著影响常见RL基准任务的学习性能。分析结果表明，部分性能差异可归因于优化景观复杂度的变化。最后，我们探讨了RL算法分析技术面临的开放挑战。