Choosing an appropriate representation of the environment for the underlying decision-making process of the \gls{RL} agent is not always straightforward. The state representation should be inclusive enough to allow the agent to informatively decide on its actions and compact enough to increase sample efficiency for policy training. Given this outlook, this work examines the effect of various state representations in incentivizing the agent to solve a specific robotic task: antipodal and planar object grasping. A continuum of state representation abstractions is defined, starting from a model-based approach with complete system knowledge, through hand-crafted numerical, to image-based representations with decreasing level of induced task-specific knowledge. We examine the effects of each representation in the ability of the agent to solve the task in simulation and the transferability of the learned policy to the real robot. The results show that RL agents using numerical states can perform on par with non-learning baselines. Furthermore, we find that agents using image-based representations from pre-trained environment embedding vectors perform better than end-to-end trained agents, and hypothesize that task-specific knowledge is necessary for achieving convergence and high success rates in robot control. Supplementary material can be found at the project webpage: https://github.com/PetropoulakisPanagiotis/igae.

选择适当的环境表示对于强化学习代理的决策过程并不总是直观的，本研究考察了不同状态表示对于激励代理解决机器人任务的影响，结果显示使用数字状态的强化学习代理与非学习基线表现相当，而使用经过预训练环境嵌入向量的图像表示的代理表现更好，推测任务特定的知识对于实现收敛和高成功率的机器人控制是必要的。

表示抽象作为强化学习智能体的激励机制: 一个机器人抓取的案例研究