Shypd.aiThe largest open index of AI toolsmpc-reinforcement-learning is an Open Source AI Agents & Automation tool that trains model-based Reinforcement Learning agents. It uses Model Predictive Control as function approximation, offering a robust framework for learning-based control.
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mpc-reinforcement-learning is an open-source Python library designed for training model-based Reinforcement Learning (RL) agents. It uniquely leverages Model Predictive Control (MPC) as a function approximation method, merging two powerful control techniques into a single data-driven approach. The library allows users to define environments, craft MPC controllers, and train them using various RL algorithms like Q-learning and Deterministic Policy Gradient (DPG). It supports both gradient-based and gradient-free optimization methods, including Bayesian Optimization. The framework is particularly effective for applications where a prediction model can be exploited to forecast environmental behavior and compute optimal actions, making it suitable for complex-to-model environments. It provides detailed documentation and examples for setting up and solving tasks, such as controlling Linear Time Invariant (LTI) systems.
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