AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
Olov Andersson, Fredrik Heintz, Patrick Doherty

Last modified: 2015-02-21


Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.


Reinforcement Learning,Gaussian Processes,Optimization,Robotics

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