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GECCO
2009
Springer

Uncertainty handling CMA-ES for reinforcement learning

8 years 11 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an adaptive uncertainty handling mechanism. Because uncertainty is a typical property of RL problems this new algorithm, termed UH-CMA-ES, is promising for RL. The UH-CMA-ES dynamically adjusts the number of episodes considered in each evaluation of a policy. It controls the signal to noise ratio such that it is just high enough for a sufficiently good ranking of candidate policies, which in turn allows the evolutionary learning to find better solutions. This significantly increases the learning speed as well as the robustness without impairing the quality of the final solutions. We evaluate the UH-CMA-ES on fully and partially observable Markov decision processes with random start states and noisy observations. A canonical natural policy gradient method and random search serve as a baseline for comparison. Categor...
Verena Heidrich-Meisner, Christian Igel
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where GECCO
Authors Verena Heidrich-Meisner, Christian Igel
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