application of scalable safe reinforcement learning to high-risk robotics

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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)