强化学习
强化学习是一种目标导向的计算学习方法，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一种最优策略，使在任务期间收到的预期累积折扣长期奖励最大化。
这种学习方法使得智能体能够在没有人工干预的情况下，通过一系列决策来最大化任务的累积奖励，而无需明确地编程来实现目标。您可以使用 Reinforcement Learning Toolbox™ 软件创建和训练强化学习智能体。
有关详细信息，请参阅What Is Reinforcement Learning? (Reinforcement Learning Toolbox)。
主题
- What Is Reinforcement Learning? (Reinforcement Learning Toolbox)
Reinforcement learning is a goal-directed computational approach where a computer learns to perform a task by interacting with an uncertain dynamic environment.
- Reinforcement Learning Workflow (Reinforcement Learning Toolbox)
Typical workflow you use to apply reinforcement learning to a problem.
- Reinforcement Learning Environments (Reinforcement Learning Toolbox)
Model environment dynamics using a MATLAB® object that generates rewards and observations in response to agents actions.
- Reinforcement Learning for Control Systems Applications (Reinforcement Learning Toolbox)
You can train a reinforcement learning agent to control a plant.
- Train Reinforcement Learning Agent in MDP Environment (Reinforcement Learning Toolbox)
Train a reinforcement learning agent in a generic Markov decision process environment.
- Train Reinforcement Learning Agent in Basic Grid World (Reinforcement Learning Toolbox)
Train Q-learning and SARSA agents to solve a grid world in MATLAB.
- Design and Train Agent Using Reinforcement Learning Designer (Reinforcement Learning Toolbox)
Design and train a DQN agent for a cart-pole system using the Reinforcement Learning Designer app.
- Create DQN Agent Using Deep Network Designer and Train Using Image Observations (Reinforcement Learning Toolbox)
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 (Reinforcement Learning Toolbox)
Train a DDPG agent using an image-based observation signal.
- Control Water Level in a Tank Using a DDPG Agent (Reinforcement Learning Toolbox)
Train a controller using reinforcement learning with a plant modeled in Simulink® as the training environment.
精选示例
Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train a DQN agent for an automated driving application using parallel computing.
Deep Reinforcement Learning for Optimal Trade Execution
Use the Reinforcement Learning Toolbox™ and Deep Learning Toolbox™ to design agents for optimal trade execution.
Train Biped Robot to Walk Using Reinforcement Learning Agents
Compare DDPG and TD3 agent for the control a biped walking robot modeled in Simscape™ Multibody™.
Train DDPG Agent for Adaptive Cruise Control
Train a DDPG agent for an adaptive cruise control application.
Train Hybrid SAC Agent for Path-Following Control
Train an hybrid SAC agent for lane following control.
Automatic Parking Valet with Unreal Engine Simulation
Use a TD3 agent with an MPC controller to perform a parking maneuver.
Train Humanoid Walker
Model a humanoid robot using Simscape Multibody™ and train it using either a genetic algorithm (which requires a Global Optimization Toolbox license) or reinforcement learning (which requires Deep Learning Toolbox™ and Reinforcement Learning Toolbox™ licenses).
