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Episodic Reinforcement Learning by Logistic Reward-Weighted Regression

Daan Wierstra; Tom Schaul; Jan Peters; Jürgen Schmidhuber
In: Vera Kurková; Roman Neruda; Jan Koutník (Hrsg.). Artificial Neural Networks - ICANN 2008 , 18th International Conference, Proceedings. International Conference on Artificial Neural Networks (ICANN-2008), September 3-6, Prague, Czech Republic, Pages 407-416, Lecture Notes in Computer Science, Vol. 5163, Springer, 2008.


It has been a long-standing goal in the adaptive control community to reduce the generically difficult, general reinforcement learning (RL) problem to simpler problems solvable by supervised learning. While this approach is today’s standard for value function-based methods, fewer approaches are known that apply similar reductions to policy search methods. Recently, it has been shown that immediate RL problems can be solved by reward-weighted regression, and that the resulting algorithm is an expectation maximization (EM) algorithm with strong guarantees. In this paper, we extend this algorithm to the episodic case and show that it can be used in the context of LSTM recurrent neural networks (RNNs). The resulting RNN training algorithm is equivalent to a weighted self-modeling supervised learning technique. We focus on partially observable Markov decision problems (POMDPs) where it is essential that the policy is nonstationary in order to be optimal. We show that this new reward-weighted logistic regression used in conjunction with an RNN architecture can solve standard benchmark POMDPs with ease.

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