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Particle Swarm Optimization for Model Predictive Control in Reinforcement Learning Environments
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Author(s): Daniel Hein (Technische Universität München, Germany), Alexander Hentschel (AxiomZen, Canada), Thomas A. Runkler (Siemens AG, Germany)and Steffen Udluft (Siemens AG, Germany)
Copyright: 2018
Pages: 27
Source title:
Critical Developments and Applications of Swarm Intelligence
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology, China)
DOI: 10.4018/978-1-5225-5134-8.ch016
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Abstract
This chapter introduces a model-based reinforcement learning (RL) approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical online optimization of control action sequences following the strategy of nonlinear model predictive control. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, particle swarm optimization (PSO) is applied to search for optimal solutions. This PSO policy (PSO-P) is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world-inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on two standard benchmarks mountain car and cart-pole swing-up and a new industry-inspired benchmark, the so-called industrial benchmark.
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