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Actor-Critic Model Predictive Control (AC-MPC)

This repository implements an Actor-Critic Model Predictive Control (AC-MPC) framework that combines the strengths of Model Predictive Control (MPC) with Reinforcement Learning (RL). The implementation includes various classic control environments and a novel dynamical system environment.

Project Overview

The project implements a hybrid control approach that integrates:

  • Model Predictive Control (MPC) for optimal trajectory planning
  • Reinforcement Learning (specifically PPO - Proximal Policy Optimization) for policy learning
  • Actor-Critic architecture for improved policy optimization

Environments

The following environments are supported:

  1. Pendulum
  2. Cart Pole (both discrete and continuous reward versions)
  3. Mountain Car
  4. Acrobot
  5. Custom Dynamical System (with configurable parameters like wind gusts, friction, etc.)

Key Components

Core Modules

  • mpc.py: Implementation of Model Predictive Control algorithms
  • policy.py: Policy networks and Actor-Critic implementations
  • system.py: System dynamics and models
  • env.py: Environment implementations and wrappers
  • costs.py: Cost functions for different environments
  • linearizer.py: System linearization utilities
  • utils.py: Utility functions and helpers

Training Scripts

  • Training scripts for each environment (e.g., train_acmpc_multienv_pendulum_args.py)
  • PPO baseline training scripts (e.g., train_ppo_pendulum.py)
  • Support for multi-environment training

Testing Scripts

  • Test scripts for each environment (e.g., test_mpc_pendulum.py)
  • Systematic evaluation scripts (e.g., test_acmpc_systematic_dynamical_system_args.py)

Features

  • Hybrid control combining MPC and RL
  • Support for multiple classic control environments
  • Configurable system parameters (friction, wind gusts, etc.)
  • Gaussian noise wrappers for robustness
  • Integration with Weights & Biases for experiment tracking
  • TensorBoard support for visualization
  • Systematic evaluation tools

Installation

  1. Create a conda environment using the provided environment files:

    # For MacOS/Linux
conda env create -f environment.yml
# For Linux-specific dependencies
conda env create -f environment_linux.yml

  2. Activate the environment:

    conda activate acmpc

Usage

Training

Train an AC-MPC agent on different environments:

# Train on Pendulum
python train_acmpc_multienv_pendulum_args.py

# Train on Cart Pole
python train_acmpc_multienv_cart_pole_args.py

# Train on Mountain Car
python train_acmpc_multienv_mountain_car_args.py

Testing

Test trained models:

# Test on Pendulum
python test_acmpc_pendulum_args.py --model_name path/to/model

# Test on Dynamical System
python test_acmpc_dynamical_system_args.py --model_name path/to/model

Key Parameters

  • n_envs: Number of parallel environments
  • prediction_horizon: MPC prediction horizon
  • num_optimization_step: Number of optimization steps in MPC
  • gaussian_noise_scale: Scale of Gaussian noise for robustness
  • Environment-specific parameters (e.g., wind gusts, friction coefficients)

Experiment Tracking

The project uses Weights & Biases for experiment tracking. Key metrics logged include:

  • Training rewards
  • Episode lengths
  • Policy gradients
  • Value function losses
  • Model checkpoints

Dependencies

Key dependencies include:

  • PyTorch
  • Stable Baselines3
  • Gymnasium
  • TensorBoard
  • Weights & Biases

For a complete list of dependencies, refer to environment.yml or environment_linux.yml.