This study presents a benchmark for evaluating action-constrained
reinforcement learning (RL) algorithms. In action-constrained RL, each action
taken by the learning system must comply with certain constraints. These
constraints are crucial for ensuring the feasibility and safety of actions in
real-world systems. We evaluate existing algorithms and their novel variants
across multiple robotics control environments, encompassing multiple action
constraint types. Our evaluation provides the first in-depth perspective of the
field, revealing surprising insights, including the effectiveness of a
straightforward baseline approach. The benchmark problems and associated code
utilized in our experiments are made available online at
github.com/omron-sinicx/action-constrained-RL-benchmark for further research
and development.

本研究提出了一种用于评估行动受限强化学习算法的基准测试，对现有算法及其新颖变种在多个机器人控制环境下进行评估，提供了领域的第一个深入视角，并揭示了令人惊讶的见解，包括普通基准方法的有效性。我们的实验中使用的基准问题和相关代码可在 github.com/omron-sinicx/action-constrained-RL-benchmark 上获得以供进一步研究和发展。

基于行为约束的机器人控制 Actor-Critic 深度强化学习算法基准测试

Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for  Robotics Control with Action Constraints

In this paper we describe our approach to the arousal and valence track of
the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild
(ABAW). We extracted facial features using OpenFace and used them to train a
multiple output random forest regressor. Our approach performed comparable to
the baseline approach.

使用 OpenFace 提取面部特征，训练多重输出随机森林回归器，与基准方法表现相似，这是关于野外情感行为分析比赛的研究。