application of scalable safe reinforcement learning to high-risk robotics

综上所述，研究进展顺利。朝着高风险控制的强化学习的成就迈出了重要的一步。正如研究计划所述，第一年的重点是解决理论问题。研究结果解决了如何利用熵进行更稳健的强化学习框架和后续风险敏感控制的基本问题。本文试图从几个不同的角度来解决问题，如直接增加鲁棒性；确保学习进步；并利用更稳定的萨利斯熵。因此，在国际顶级会议上发表/提交了5篇论文：[1]谨慎的演员评论家，亚洲机器学习会议2021；几何值迭代-用于强化学习的动态误差感知KL正则化，2021年亚洲机器学习会议；q-Munchausen强化学习，人工智能中的不确定性2022（正在审查中）；[10]基于优势学习的最大Tsallis熵框架中的KL正则化，人工智能中的不确定性，2022（正在审查中）；下界最大化单调政策改进，人工智能的不确定性2022（正在审查中）

As a summary the progress of research has been going well. Important steps towards the achievements of reinforcement learning for high-risk control have been made as planned. As stated in the research plan, the first year focuses on solving the theoretical problems. The results solved the fundamental problem of how to make use of entropy for more robust reinforcement learning framework and subsequent risk-sensitive control. The works attempted to tackle the problem from several different perspectives such as increasing the robustness directly; ensuring learning improvement; and making use of more stable Tsallis entropy.As a result, the following papers have been published/submitted 5 papers to top international conferences: [1] Cautious Actor Critic, Asian Conference on Machine Learning 2021; [2] Geometric Value Iteration - Dynamic Error Aware KL Regularization for Reinforcement Learning, Asian Conference on Machine Learning 2021; [3] q-Munchausen Reinforcement Learning, Uncertainty in Artificial Intelligence 2022 (under review); [4] Enforcing KL Regularization in Maximum Tsallis Entropy Framework via Advantage Learning, Uncertainty in Artificial Intelligence 2022 (under review); [5] Lower Bound Maximizing Monotonic Policy Improvement, Uncertainty in Artificial Intelligence 2022 (under review)