التفاصيل البيبلوغرافية
| العنوان: |
基于多任务强化学习的地形自适应模仿学习方法. (Chinese) |
| Alternate Title: |
Terrain-Adaptive Motion Imitation Based on Multi-task Reinforcement Learning. (English) |
| المؤلفون: |
余 昊, 梁宇宸, 张 驰, 刘跃虎 |
| المصدر: |
Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li; Sep2024, Vol. 39 Issue 5, p1182-1191, 10p |
| Abstract (English): |
Terrain adaptive ability is the basis for the stable movement of agents under complex terrain conditions. Due to the complexity of the dynamical systems of these agents, such as humanoid robots, it is usually difficult for traditional inverse dynamics methods to have such ability. Recent research has used the advantages of reinforcement learning in solving sequential decision-making problems to train agents to adapt to terrain. However, these single-task learning methods cannot effectively learn the correlation in various terrains. In fact, complex terrain adaptive tasks can be considered as a multi-task problem, and the relationship between sub-tasks can be measured by different terrain factors. And then, the problem of incomplete acquisition of data distribution information can be solved by mutual learning of sub-task models. Therefore, this paper proposes a multi-task reinforcement learning method. It contains an execution layer which is consist of pre-trained subtask models and a decision layer based on reinforcement learning method. Moreover, the decision layer uses soft constraints to fuse models of the execution layer. Experiments on LeggedGym terrain simulator prove that the agent trained by the method in this paper is more stable in movement and has fewer falls down on complex terrains, showing better generalization performance. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
地形自适应能力是智能体在复杂地形条件下稳定运动的基础, 而由于机器人动力学系统的复 杂性, 传统逆动力学方法通常难以使其具备这种能力。现有利用强化学习在解决序列决策问题上的优 势训练智能体地形适应能力的单任务学习方法无法有效学习各类地形中的相关性。事实上, 复杂地形 自适应任务可以认为是一种多任务, 子任务间的关系可以用不同地形影响因素来衡量, 通过子任务模 型的相互学习解决数据分布信息获取不全面的问题。基于此, 本文提出一种多任务强化学习方法。该 方法包含 1 个由子任务预训练模型组成的执行层和 1 个基于强化学习方法、采用软约束融合执行层模 型的决策层。在 LeggedGym 地形仿真器上的实验证明, 本文方法训练的智能体运动更加稳定, 在复杂 地形上的摔倒次数更少, 并且表现出更好的泛化性能。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |