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摘要: 随着高维特征表示与逼近能力的提高, 强化学习(Reinforcement learning, RL)在博弈与优化决策、智能驾驶等现实问题中的应用也取得显著进展. 然而强化学习在智能体与环境的交互中存在人工设计奖励函数难的问题, 因此研究者提出了逆强化学习(Inverse reinforcement learning, IRL)这一研究方向. 如何从专家演示中学习奖励函数和进行策略优化是一个重要的研究课题, 在人工智能领域具有十分重要的研究意义. 本文综合介绍了逆强化学习算法的最新进展, 首先介绍了逆强化学习在理论方面的新进展, 然后分析了逆强化学习面临的挑战以及未来的发展趋势, 最后讨论了逆强化学习的应用进展和应用前景.Abstract: With the research and development of deep reinforcement learning, the application of reinforcement learning (RL) in real-world problems such as game and optimization decision, and intelligent driving has also made significant progress. However, reinforcement learning has difficulty in manually designing the reward function in the interaction between an agent and its environment, so researchers have proposed the research direction of inverse reinforcement learning (IRL). How to learn reward functions from expert demonstrations and perform strategy optimization is a novel and important research topic with very important research implications in the field of artificial intelligence. This paper presents a comprehensive overview of the recent progress of inverse reinforcement learning algorithms. Firstly, new advances in the theory of inverse reinforcement learning are introduced, then the challenges faced by inverse reinforcement learning and the future development trends are analyzed, and finally the progress and application prospects of inverse reinforcement learning are discussed.
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图 2 MDP ((a)和(c)是确定性MDP;(b)和(d)是随机性MDP)
Fig. 2 MDP ((a) and (c) are the deterministic MDP; (b) and (d) are the stochastic MDP)
图 7 最大熵深度逆强化学习的结构
Fig. 7 Structure of maximum entropy deep inverse reinforcement learning
图 8 基于序列专家演示的逆强化学习进程
Fig. 8 The inverse reinforcement learning process based on sequential expert demonstration
图 9 估计奖励函数的神经网络模型结构
Fig. 9 Structure of the neural network model for estimating the reward function
图 12 利用深度最大熵逆强化学习轨迹规划结构图
Fig. 12 Structure of trajectory planning using deep maximum entropy IRL
表 1 逆强化学习算法的研究历程
Table 1 Timeline of inverse reinforcement learning algorithm
逆强化学习算法 面临的挑战 解决的问题 作者 (年份) 有限和大状态空间的MDP/R问题 Ng等[9] (2000) 线性求解MDP/R问题 Abbeel等[11] (2004) 基于边际的逆强化学习 模糊歧义 策略的最大化结构与预测问题 Ratliff等[12] (2006) 复杂多维任务问题 Bogdanovic等[22] (2015) 现实任务的适用性问题 Hester等[23] (2018) 基于贝叶斯的逆强化学习 先验知识的选取难、计算复杂 结合先验知识和专家数据推导奖励的概率分布问题 Ramachandran等[21] (2007) 基于概率的逆强化学习 在复杂动态环境中适应性差 最大熵约束下的特征匹配问题 Ziebart等[13] (2008) 转移函数未知的MDP/R问题 Boularias等[14] (2011) 基于高斯过程的逆强化学习 计算复杂 奖励的非线性求解问题 Levine[19]等 (2011) 基于最大熵的深度逆强化学习 计算复杂、过拟合、专家
演示数据不平衡、有限从人类驾驶演示中学习复杂城市环境中奖励的问题 Wulfmeier等[15] (2016) 从数据中提取策略的对抗性逆强化学习问题 Ho等[18] (2016) 多个奖励稀疏分散的线性可解非确定性MDP/R问题 Budhraja等[59] (2017) 自动驾驶车辆在交通中的规划问题 You等[38] (2019) 无模型积分逆RL的奖励问题 Lian等[70] (2021) 利用最大因果熵推断奖励函数的问题 Gleave等[94] (2022) 基于神经网络的逆强化学习 过拟合、不稳定 具有大规模高维状态空间的自动导航的IRL问题 Chen等[62] (2019) 
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