Reinforcement Learning for Inverted Pendulum Control

Overview

This project involved applying Reinforcement Learning (RL) algorithms to control both single and double inverted pendulum systems. Using algorithms such as Q-Learning, DQN, DDPG, and PPO, we implemented controllers to achieve swing-up and stabilization tasks. The project explored the dynamic complexities of inverted pendulum systems and highlighted the effectiveness of RL techniques for non-linear control problems.

Results

• Single Inverted Pendulum:
  • Achieved a 100% success rate for swing-up and stabilization tasks under ideal conditions.
  • Maintained a 90% success rate under noisy conditions with a simulation time of 30 seconds.
• Double Inverted Pendulum:
  • Successfully stabilized the pendulum but encountered challenges in achieving swing-up with model-free RL methods.
• Performance Metrics:
  • Trained RL models for swing-up and stabilization tasks in under 50,000 episodes.
  • Demonstrated the effectiveness of custom reward functions for dynamic control tasks.

Report PDF | GitHub (Chinese README)

Technical Details

• Algorithms Applied:
  • Q-Learning and DQN: Explored discrete action spaces for initial experiments.
  • A2C and PPO: Achieved robust performance for stabilization tasks in continuous action spaces.
  • DDPG: Provided smooth control for swing-up tasks with deterministic policy gradients.
• Custom Toolkit:
  • Developed RL agents from scratch using PyTorch, including functions for initialization, model updates, and action sampling.
  • Designed visualization tools to monitor reward curves and training metrics.
• Reward Design:
  • Swing-up Task: Rewarded higher pendulum angles while penalizing velocity at the peak.
  • Stabilization Task: Encouraged minimal deviation from the vertical position and low angular velocity.

Challenges

• Swing-Up Task:
  • Coordinating motion during the throw-and-catch process was challenging, especially under noisy conditions.
  • Solution: Implemented collaborative agents for swing-up and stabilization, with separate reward functions for each sub-task.
• Double Inverted Pendulum:
  • Model-free RL struggled with the system’s chaotic behavior.
  • Solution: Transitioned to model-based approaches like PILCO for better state-action-reward predictions.

Reflection and Insights

This project deepened my understanding of reinforcement learning and its application to real-world control problems. It highlighted the importance of tailored reward functions and robust algorithm selection for dynamic systems. The challenges in handling chaotic behaviors inspired further exploration into model-based strategies to enhance RL performance.

Team and Role

• Team: Worked collaboratively with two teammates on RL model implementation and evaluation.
• My Role:
  • Focused on the single inverted pendulum tasks, including algorithm selection and reward function design.
  • Developed custom RL agents using PyTorch, optimizing hyperparameters for efficient training.
  • Led the implementation of the collaborative “throw-catch” process for swing-up tasks.