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Balancing inverted pendulum using reinforcement algorithms | IEEE Conference Publication | IEEE Xplore

Balancing inverted pendulum using reinforcement algorithms


Abstract:

With the advancements in technology, robots has become systems that can learn and achieve complex behaviors in real life with the help of machine learning algorithms. Amo...Show More

Abstract:

With the advancements in technology, robots has become systems that can learn and achieve complex behaviors in real life with the help of machine learning algorithms. Among those algorithms, reinforcement learning algorithms are widely used in robotics to teach the systems by trials and errors. In this work, our goal is to use the two different reinforcement algorithms, Q-learning and Adaptive Heuristic Critic (AHC) algorithm, on well-known cart-pole balancing problem and examine the performance results. We used Box2d physics engine simulator to simulate the cart-pole model and the environment. Observing the experimental results, AHC algorithm was able to balance the system for more step counts than Q-learning algorithm.
Date of Conference: 16-19 May 2016
Date Added to IEEE Xplore: 23 June 2016
ISBN Information:
Conference Location: Zonguldak, Turkey