用强化学习训练四足机器人，仅靠一台电脑就实现高效步态策略。
Quadruped Robot Simulation Using Deep Reinforcement Learning -- A step towards locomotion policy
- 基于马尔可夫决策过程建模，结合PPO算法优化控制策略。
- 在模拟环境中训练数千次时间步，达成稳定行走性能。
- 开源框架支持初学者快速部署自定义算法与配置。
本文提出一种新型强化学习方法，用于在模拟环境中训练四足机器人。该方法针对动态环境下四足机器人控制的挑战，设计了最优策略与高效训练方案，在资源有限条件下仍表现出良好性能。实验采用raisimGymTorch开源库和专有软件RaiSim进行ANYmal机器人的仿真。核心思路是将步态评估融入马尔可夫决策过程建模，并使用基于演员-评论家结构的近端策略优化（PPO）算法求解。所有训练过程仅需单台桌面机完成，收集了数千条状态转移数据。所提出的控制器在模拟环境中经数千时间步训练后展现出稳定运动能力，为早期研究者提供了可快速部署自定义算法与配置的基础平台。
原文摘要 · Abstract (English)
We present a novel reinforcement learning method to train the quadruped robot in a simulated environment. The idea of controlling quadruped robots in a dynamic environment is quite challenging and my method presents the optimum policy and training scheme with limited resources and shows considerable performance. The report uses the raisimGymTorch open-source library and proprietary software RaiSim for the simulation of ANYmal robot. My approach is centered on formulating Markov decision processes using the evaluation of the robot walking scheme while training. Resulting MDPs are solved using a proximal policy optimization algorithm used in actor-critic mode and collected thousands of state transitions with a single desktop machine. This work also presents a controller scheme trained over thousands of time steps shown in a simulated environment. This work also sets the base for early-stage researchers to deploy their favorite algorithms and configurations. Keywords: Legged robots, deep reinforcement learning, quadruped robot simulation, optimal control
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