用强化学习解决最小代价到达避障问题，成本降低57%。
Solving Minimum-Cost Reach Avoid using Reinforcement Learning
- 基于哈密顿-雅可比可达性设计新算法RC-PPO
- 在Mujoco上实现57%更低的累积代价
- 适合需要高效路径规划的机器人应用
现有强化学习方法无法直接求解最小代价到达避障问题，因其优化结构与现有方法不兼容。通常采用加权求和的代理目标，导致策略次优。本文提出RC-PPO，利用哈密顿-雅可比可达性理论，构建强化学习框架。在Mujoco模拟器上的多组最小代价到达避障基准测试中，RC-PPO达到与现有方法相当的到达率，同时累积代价最高降低57%。项目主页：https://oswinso.xyz/rcppo。
原文摘要 · Abstract (English)
Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints of reaching the goal and avoiding unsafe states, as the structure of this new optimization problem is incompatible with current methods. Instead, a surrogate problem is solved where all objectives are combined with a weighted sum. However, this surrogate objective results in suboptimal policies that do not directly minimize the cumulative cost. In this work, we propose RC-PPO, a reinforcement-learning-based method for solving the minimum-cost reach-avoid problem by using connections to Hamilton-Jacobi reachability. Empirical results demonstrate that RC-PPO learns policies with comparable goal-reaching rates to while achieving up to 57% lower cumulative costs compared to existing methods on a suite of minimum-cost reach-avoid benchmarks on the Mujoco simulator. The project page can be found at https://oswinso.xyz/rcppo.
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