.png){kind=icon}
返回
Symbolic Regression Methods for Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3119000.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering little insight into the mappings learned, and they require extensive trial and error tuning of their hyper-parameters. In this paper, we propose a new approach to constructing smooth value functions in the form of analytic expressions by using symbolic regression. We introduce three off-line methods for finding value functions based on a state-transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation. The methods are illustrated on four nonlinear control problems: velocity control under friction, one-link and two-link pendulum swing-up, and magnetic manipulation. The results show that the value functions yield well-performing policies and are compact, mathematically tractable, and easy to plug into other algorithms. This makes them potentially suitable for further analysis of the closed-loop system. A comparison with an alternative approach using neural networks shows that our method outperforms the neural network-based one.
Keyword:
Mathematical models
Reinforcement learning
Genetic programming
Numerical models
Approximation algorithms
Tuning
Training
Reinforcement learning
value iteration
policy iteration
symbolic regression
genetic programming
nonlinear optimal control
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Genome-wide association analysis for grain moisture content and dehydration rate on maize hybridsOAAI
.png){kind=icon}
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏
分享收藏
查看更多内容