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使用深度神经网络的强化学习

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。

有关详细信息,请参阅Reinforcement Learning Using Deep Neural Networks

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 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.

特色示例