论文标题
偶然受限的迭代线性季节随机游戏
Chance-Constrained Iterative Linear-Quadratic Stochastic Games
论文作者
论文摘要
动态游戏是用于多机器人计划的强大范式，安全限制满意度至关重要。受限的随机游戏特别令人感兴趣，因为现实世界的机器人需要在不确定性下操作和满足限制。现有的解决随机游戏的方法使用手工调整重量的指数罚款处理机会限制。但是，找到合适的罚款权重是不平凡的，需要试用和错误。在本文中，我们提出了机会约束的迭代线性季节随机游戏（CCILQGAMES）算法。 CCILQGAMES使用增强的Lagrangian方法解决了机会约束的随机游戏。我们在三种自主驾驶场景中评估了算法，包括合并，交叉路口和回旋处。实验结果和蒙特卡洛测试表明，CCILQGAMES可以在随机环境中产生安全且交互式策略。
Dynamic game arises as a powerful paradigm for multi-robot planning, for which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, as real-world robots need to operate and satisfy constraints under uncertainty. Existing methods for solving stochastic games handle chance constraints using exponential penalties with hand-tuned weights. However, finding a suitable penalty weight is nontrivial and requires trial and error. In this paper, we propose the chance-constrained iterative linear-quadratic stochastic games (CCILQGames) algorithm. CCILQGames solves chance-constrained stochastic games using the augmented Lagrangian method. We evaluate our algorithm in three autonomous driving scenarios, including merge, intersection, and roundabout. Experimental results and Monte Carlo tests show that CCILQGames can generate safe and interactive strategies in stochastic environments.
