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.

Model Based Policy Optimization. Reviewing recent advances in

Reinforcement Learning Toolbox - MATLAB

Optimization of Simulink Model Parameters

Train Multiple Agents for Area Coverage - MATLAB & Simulink

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

Create Policies and Value Functions - MATLAB & Simulink

Why Choose Model-Based Reinforcement Learning? Video - MATLAB

Reinforcement Learning Toolbox - MATLAB

Model-Based Reinforcement Learning Using Custom Training Loop

Model-Based Policy Optimization (ICML Workshops)

Train MBPO Agent to Balance Cart-Pole System - MATLAB & Simulink

Train Multiple Agents to Perform Collaborative Task - MATLAB

Training and Simulation - MATLAB & Simulink - MathWorks 中国

Companion resources to Why Choose Model-Based Reinforcement