Neuroevolution strategies for episodic reinforcement learning

By Verena Heidrich-Meisner, Christian Igel. 2009

95 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “2009 also saw a GPU-DBN implementation (Raina, Madhavan, & Ng, 2009) orders of magnitudes faster than previous CPU-DBNs (see Section 5.15); see also Coates et al. (2013).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1, except that C is now an FNN or RNN trained by evolutionary algorithms [200, 255, 105, 56, 68] applied to NNs [165, 321, 180, 259, 72, 90, 89, 110, 94], or by policy gradient methods [314, 315, 316, 274, 18, 1, 63, 128, 313, 210, 192, 191, 256, 85, 312, 190, 82, 93][245, Sec.”
Variational Inference for Policy Search in changing situations (2011, methodology): “Many policy search algorithms (Kober & Peters, 2009; Vlassis et al., 2009; Heidrich-Meisner & Igel, 2009) use inference or inference related methods to iteratively optimize the policy.”
Evolutionary Computation for Reinforcement Learning (2012, methodology): “However , in a more recent study, CMA-NeuroES (see Section 2) performed even better (Heidrich-Meisner and Igel, 2009b).”
Policy Optimization by Genetic Distillation (2017, methodology): “CMA-ES has been used to learn neural network policies for reinforcement learning (CMA-NeuroES, Heidrich-Meisner & Igel (2009); Igel (2003)).”
Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm (2014, methodology): “In this paper, we present a preference-based extension of evolutionary direct policy search (EDPS) as proposed by Heidrich-Meisner and Igel [18,19].”
A Survey on Policy Search for Robotics (2013, background): “Step-based exploration strategies use different exploration noise at each time-step and can explore either in action space or in parameter space as we know from the discussion in the previous section.”