Model-Based Policy Optimization (MBPO) Agents - MATLAB & Simulink

A model-based (MBPO) reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.

Companion resources to Why Choose Model-Based Reinforcement

Train Multiple Agents for Area Coverage - MATLAB & Simulink

Model-Based Reinforcement Learning Using Custom Training Loop

Model-Based Reinforcement Learning Using Custom Training Loop

Train Multiple Agents for Area Coverage - MATLAB & Simulink

Optimization of Simulink Model Parameters

Why Choose Model-Based Reinforcement Learning? Video - MATLAB

Reinforcement Learning Toolbox - MATLAB

Reinforcement Learning Environments - MATLAB & Simulink

Simulink Design Optimization - MATLAB

Model-Based Policy Optimization (ICML Workshops)

Training and Simulation - MATLAB & Simulink - MathWorks 中国

Simulink Optimizations and Model Coverage - MATLAB & Simulink

Model-Based Design Toolbox