增强学习

通过与未知动态环境交互来训练深度神经网络智能体

强化学习是一种目标导向的计算方法，其中智能体通过与未知动态环境交互来学习执行任务。在训练期间，学习算法会更新智能体策略参数。学习算法的目标是找到最佳策略，最大化在任务期间获得的长期回报。

根据智能体的类型，策略可表示为一个或多个策略和价值函数。您可以使用深度神经网络来实现这些表示。然后可以使用 Reinforcement Learning Toolbox™ 软件训练这些网络。

主题

Reinforcement Learning Using Deep Neural Networks
Reinforcement learning is a goal-directed computational approach where a computer learns to perform a task by interacting with an unknown dynamic environment.
Create Simulink Environment and Train Agent
Train a controller using reinforcement learning with a plant modeled in Simulink^® as the training environment.
Create DQN Agent Using Deep Network Designer and Train Using Image Observations
Create a reinforcement learning agent using the Deep Network Designer app from the Deep Learning Toolbox™.
Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation
Train a reinforcement learning agent using an image-based observation signal.
Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train a reinforcement learning agent for a lane keeping assist application.
Imitate MPC Controller for Lane Keeping Assist
Train a deep neural network to imitate the behavior of a model predictive controller.