A survey of reinforcement learning solutions to the inverted pendulum problem.
The inverted pendulum problem can be defined concisely as creating a system that autonomously balances a rotating pendulum attached to a cart on a rail using actuators to move the cart along the rail, and sensors to reveal the state of the cart and pendulum. Given a specific inverted pendulum system, solving the problem amounts to choosing which sensors to use, optionally formulating a deterministic or stochastic model that approximates the physics involved, and most importantly figuring out a control strategy that works. It is a classic problem in control theory and dynamics and can serve as a good testing ground for the development of real-time control algorithms. A good argument for the motivation of understanding the problem can be seen by noting its prevalence in nature and the man made world. For example, every person when upright needs to constantly make adjustments to prevent from falling over, and so all of us repeatedly solve a much more difficult version of this problem when sitting or moving about.
A pendulum of length l with a mass m on one end and attached to a cart of mass M with a hinge, is able to rotate by the effect of the application of some force F on the cart.
By assumption, the friction of the cart on the ground as well as the friction of the pendulum on the cart are ignored. The cart is limited to motion in the horizontal x-axis, while the pendulum is able to rotate along the x, y plane freely, making an angle of θ with the y-axis. It is also assumed that the force vector F has no component in the y direction. A controller attempts to balance the pendulum by applying a finite force to the cart, allowing it to move left or right with some acceleration. After some amount of time ∆T has passed, the controller fails if either the angle of the pendulum deviates by more than ±∆θ or the position of the cart reaches the bounds ±L. The problem can now be stated explicitly: develop a controller that balances the pendulum under these constraints.
The following list contains background research material.
[1] Stimac, Andrew K. Standup and stabilization of the inverted pendulum. Diss. Massachusetts Institute of Technology, 1999.
[2] Russell, Stuart, and Peter Norvig. "Artificial intelligence: a modern approach." (1995).
[3] Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. Vol. 1. No. 1. Cambridge: MIT press, 1998.
[4] Melo, Francisco S., Sean P. Meyn, and M. Isabel Ribeiro. "An analysis of reinforcement learning with function approxi- mation." Proceedings of the 25th international conference on Machine learning. ACM, 2008.
[5] Kober, J. & Peters, J. (2012). Reinforcement learning in robotics: A survey. In Reinforcement Learning, Vol. 12 of Adaptation, Learning, and Optimization, pp. 579-610. Springer Berlin Heidelberg.
[6] Anderson, Charles W. "Learning to control an inverted pendulum using neural networks." Control Systems Magazine, IEEE 9.3 (1989): 31-37.
[7] Watkins, Christopher JCH, and Peter Dayan. "Q-learning." Machine learning 8.3-4 (1992): 279-292.
[8] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural net- works." Advances in neural information processing systems. 2012.
[9] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
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